Economic Evaluation of CO2 Storage and 

Sink Enhancement Options

Final Technical Report 

Reporting Period Start Date: April 1, 2002 

Reporting Period End Date: February 28, 2003 

Principal Authors: 

Bert Bock, Tennessee Valley Authority 

Richard Rhudy, Electric Power Research Institute 

Howard Herzog, Massachusetts Institute of Technology 

Michael Klett, Parsons Infrastructure and Technology 

John Davison, IEA GHG R&D Programme 

Danial G. De La Torre Ugarte, University of Tennessee 

Dale Simbeck, SFA Pacific 

Prime Contractor: TVA, P.O. Box 1010, Muscle Shoals, AL 35662

Subcontractors: 

Parsons Infrastructure and 

Technology, Inc. 

1 Meridian Boulevard, Suite 2B-1 

Wyomissing, PA 19610 

University of Tennessee 

Agricultural Policy Analysis Center 

310 Morgan Hall 

Knoxville, TN 37996 

Electric Power Research Institute 

P.O. Box 10412 

Palo Alto, CA 94303 

Massachusetts Institute of Technology 

MIT Energy Laboratory 

Room E40-471 

Cambridge, MA 02139 

IEA Greenhouse Gas R&D 

Programme 

CRE Group Ltd., Stoke Orchard 

Cheltenham, Gloucestershire 

GL52 4RZ, United Kingdom 

SFA Pacific 

444 Castro Street, Suite 920 

Mountain View, CA 94041 

February 2003 

DE-FC26-00NT40937 

Submitted by TVA Public Power Institute 

ii

DISCLAIMER

This report was prepared as an account of work sponsored by an agency of the United 

States Government. Neither the United States Government nor any agency thereof, nor any 

other employees, makes any warranty, express or implied, or assumes any legal liability or 

responsibility for the accuracy, completeness, usefulness of any information, apparatus, 

product, or process disclosed, or represents that its use would not infringe privately owned 

rights. Reference herein to any specific commercial product, process, or service by trade 

name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its 

endorsement, recommendation, or favoring by the United States Government or any agency 

thereof. The views and opinions of authors expressed herein do not necessarily state or 

reflect those of the United States Government or any agency thereof.

iii

ABSTRACT

This project developed life-cycle costs for the major technologies and practices under 

development for CO2 storage and sink enhancement. The technologies evaluated included 

options for storing captured CO2 in active oil reservoirs, depleted oil and gas reservoirs, deep 

aquifers, coal beds, and oceans, as well as the enhancement of carbon sequestration in forests and 

croplands. The capture costs for a nominal 500 MWe integrated gasification combined cycle 

plant from an earlier study were combined with the storage costs from this study to allow 

comparison among capture and storage approaches as well as sink enhancements.

Background

In order to plan for potential CO2 mitigation mandates, energy companies need better cost 

information on CO2 mitigation options, especially storage and sink enhancement options that 

involve non-energy company operations. One of the major difficulties in evaluating CO2 storage 

and sink enhancement options is obtaining consistent, transparent, accurate, and comparable 

economics.

Objective

To develop consistent, transparent, accurate, and comparable economics of direct and indirect 

sequestration of CO2 to allow comparison between alternatives.

Approach

An international group of experts developed economic premises and cost evaluations to allow the 

comparison of a wide range of CO2 sequestration options. The project team calculated material 

balances, developed equipment lists and prices, and estimated installation costs. Economic 

evaluations for this project were estimated to ±25 to 30 percent, with the ability to modify values 

to be relatively site-specific. The economics of sequestration in this project are consistent with 

economics of advanced power generation with CO2 capture that the Electric Power Research 

Institute is developing for DOE. The economic framework also includes life cycle analysis for 

the various sequestration options— all greenhouse gas emissions from cradle to grave were 

estimated and considered in the analysis. The economic analysis used spreadsheet models that 

are flexible enough to allow a wide variation in the range of parameters to be evaluated and the 

sensitivity cases to be run. The carbon equivalent (CE) life-cycle greenhouse gas (LC GHG) 

emissions avoided by the capture and storage options assume CO2 capture and injection into 

storage reservoirs during the 20-year book life of the capture and storage plants and storage of 

injected CO2 for another 80 years.
iv

Results

Costs for the base cases ranged from $-43 to 39/tonne of CO2 equivalent LC GHG avoided. The 

forestry options are the least expensive except for the U.S. Pine case. Several forestry options 

would generate a positive return on investment. Next least expensive are the active oil reservoir 

and coal bed options followed by most of the cropland options. The other capture and storage 

options tend to be more expensive. 

Base-case forest and cropland areas required to offset LC GHG emissions avoided by base-case 

CO2 capture and storage are generally ten times more for cropland than forest land.

Limits on Interpretation

Making these comparisons was a challenge due to the differences between direct and indirect 

cases. The resulting numbers should only be used as a relative indication of cost. Site-specific 

characteristics could significantly change the outcome of the comparisons. In this study, the 

forestry options are very attractive along with capture using active oil reservoirs for storage. 

Other factors, such as land availability and storage option location and capacity will also play a 

significant part in which options make the most sense at a given site.

The current work should be considered only a starting point. Better cost information and 

improvements to processes are likely to be developed and need to be incorporated into updates of 

this information. In addition, there are plans to update and add to portions of the current work to 

answer questions that arose but were beyond the scope of the current effort. For example, what 

happens to the costs if the power plant operates for longer than the plant book life? Also, how do 

the economics look for other power generation options? These and other questions will be 

evaluated in updates to this work.
v

ACKNOWLEDGMENTS 

The Tennessee Valley Authority would like to thank the Sequestration Product Line at the U.S. 

Department of Energy National Energy Technology Laboratory for their primary funding of this 

project and for their technical input and guidance. 

We would like to thank EPRI (especially Richard Rhudy) for co-funding and significant roles in 

proposal and report preparation in addition to technical support throughout the project. 

In addition to the lead investigators listed on the cover page, we would like to thank Gemma 

Heddle of MIT for her extensive and effective involvement in assessment of the CO2 storage 

options, J. David Wallace of TVA for his outstanding assistance in development of spreadsheet 

models for this project, and Chad Hellwinckel of the University of Tennessee for his valuable 

contributions in assessing the cropland options.

vi

EXECUTIVE SUMMARY

Introduction

In order to plan for potential CO2 mitigation mandates, energy companies need better 

information on the costs of alternative CO2 mitigation options, especially storage and sink 

enhancement options that involve non-energy company operations. One of the major difficulties 

in evaluating CO2 storage and sink enhancement options is obtaining consistent, transparent, 

accurate, and comparable economics. This project compares the economics of major 

technologies and practices under development for CO2 storage and sink enhancement, including 

options for storing captured CO2, such as active oil reservoirs, depleted oil and gas reservoirs, 

deep aquifers, coal beds, and oceans, as well as the enhancement of biological sinks such as 

forests and croplands.

The primary funding source for the project was the Department of Energy (DOE). The 

Tennessee Valley Authority (TVA) and the Electric Power Research Institute (EPRI) also 

provided additional funds. An international group of experts was assembled to develop the 

technology/ practice designs, the economic premises, and cost evaluations. TVA was the prime 

contractor for this project, responsible for overall completion of the effort. EPRI organized 

efforts to select specific sequestration processes to be evaluated for captured CO2 and 

coordinated the efforts of consultants from MIT, SFA Pacific, and the IEA Greenhouse Gas 

Programme to develop and refine the framework for the economic evaluations. MIT and Parsons 

Infrastructure and Technology developed process designs for captured CO2 storage processes 

and helped TVA develop economic models for comparing technologies and practices. The 

University of Tennessee Agricultural Policy Research Center, in collaboration with TVA, 

evaluated the economics of enhancing CO2 sequestration in croplands. The IEA Greenhouse Gas 

Programme and the Edinburgh Centre for Carbon Management (ECCM) developed the concept 

design for evaluating the forest sequestration options.

Economic Framework

Most of the cost comparisons to date have concentrated on CO2 capture, with the assumption that 

CO2 sequestration is a small part of these costs. In addition, these comparisons have used 

information supplied from studies of specific technologies, and the variability in costs due to 

variability in assumptions and lack of visibility into assumptions lessens the usefulness of the 

results. In the case of sequestration, virtually no comparative economic evaluations of processes 

have been done.
vii

Methodologies for developing economic comparisons are generally available. They range from 

very detailed ±10 percent for site-specific evaluations, where final decisions are made between 

options, to very general economics with little insight into the economic premises that were used 

to develop the economics. The latter is usually a simplification of more detailed economics for 

very high-level comparisons. In some cases, probability analyses are included to help evaluate 

risks. This usually adds significantly to the complexity of the model and the time to develop 

results. The model may use simplified economics to allow probability analysis without making 

the model too complex to run in a reasonable time.

The economic evaluations developed for this project are between the ranges described above and 

are typical of prior EPRI economics where a ±25 to 30 percent estimate is made, with the ability 

to modify values to be relatively site-specific. Material balances are made, equipment lists and 

pricing are developed, and installation costs are estimated. These types of evaluations are 

intended to be transparent, consistent, and comparable. They are consistent with the EPRI 

economics of advanced power generation with CO2 capture being developed for DOE. 

Probability analysis was not included to keep the results consistent with other EPRI studies.

The economic framework also included life cycle analysis for the various sequestration options. 

This means that all greenhouse gas emissions from cradle to grave were estimated and 

considered in the analysis. The economic analysis used spreadsheet models that are flexible 

enough to allow a wide variation in the range of parameters to be evaluated and the sensitivity 

cases to be run. The CO2 storage options were sized to accommodate the CO2 from our base case 

capture plant (see the design basis in the CO2 capture, transport and storage section). In addition, 

the amount of forest and cropland required to sequester the same amount of CO2 over a 100-year 

planning horizon was calculated.

Concepts Evaluated

The potential processes and concepts to be evaluated were initially prioritized, and the concepts 

were placed into three categories—(1) included, (2) may be included but more information is 

needed before a final decision can be made, and (3) not included due to the lack of good 

information at this time. Because one of the most unique aspects of this work is the comparison 

between storage of captured CO2 and sink enhancement, it was felt that at least one of each type 

should be included. The list of concepts and their final status is presented in Table ES-1.
viii

Table ES-1

Concepts status

Included Not Included

Aquifers Ocean Fertilization

Oil Reservoirs Mineralization

Depleted Oil and Gas 

Reservoirs

Ocean Storage

Forests

Croplands

Coal Beds

In the case of ocean fertilization and mineralization, it is felt that at this time there is not enough 

reasonable information to develop a meaningful concept description and that these processes 

should not be included until more R&D is performed.

The following three sections cover the results of the cases for (1) CO2 capture, transport and 

storage, (2) forestry, and (3) croplands. The last section presents the comparisons between cases.

CO2 Capture, Transport and Storage

Overview

This section summarizes the basis of design, methodology, and results for calculating the costs 

for transport and storage of captured CO2. Additional details and background information are 

contained in Chapters 3-9. At the end of this section, the capture costs from the DOE/EPRI study 

referenced in the next section are combined to get the capture, transport and storage costs.

In conjunction with overland transport via pipeline, the following storage options were 

evaluated:

• Enhanced oil recovery

• Enhanced coalbed methane recovery

• Depleted oil reservoir storage

• Depleted gas reservoir storage

• Deep saline aquifer storage

• Ocean storage via pipeline

• Ocean storage via tanker

For each option, a baseline conceptual design was generated. From the baseline conceptual 

design, capital and O&M costs, and an economic analysis with several figures of merits were 

developed in a spreadsheet format. These were then used to develop sensitivity analyses and life 

cycle analyses, again in a spreadsheet format.
ix

Design Basis

A nominal 500 MWe gross integrated gasification combined cycle (IGCC) plant operating at an 

80 percent capacity factor was utilized as the production source of CO2. This was based on the 

DOE/EPRI’s recent study on the “Evaluation of Innovative Fossil Fuel Power Plants with CO2

Removal”1. Table ES-2 shows a summary of the parameters used in this study taken from the 

DOE/EPRI report for Case 3a, “IGCC with CO2 Removal”.

 

1

 “Evaluation of Innovative Fossil Fuel Power Plants with CO2 Removal,” EPRI Report No. 1000316, Interim 

Report, December 2000, Cosponsors: U.S. Department of Energy/Office of Fossil Energy and U.S. Department of 

Energy/NETL, EPRI Project Manager N. A. H. Holt.
x

Table ES-2

Summary of parameters for IGCC power plant with CO2 removal

Parameter Unit Value

Thermal Input, HHV 106 Btu/h 3,723

Gross Power Output MW 490.4

Net Power Output MW 403.5

Efficiency, HHV % 37.0

Capacity Factor % 80

CO2 Captured t/d 7,389

million scm/d 3.76

CO2 Emitted kg/kWh 0.073

CO2 Avoided in Capture t/d 6,246

CO2 Capture Cost $/t captured 14.55

CO2 Capture Cost $/t avoided in capture 17.21

Plant Life y 20

Capital Charge Factor % 15.0

Fuel Cost $/GJ 1.18

Fuel Real Esc. Rate %/y 0.00

Fuel Levelization Factor 1.00

TPC $/kW 1,642

Fixed O&M $/kW-y 32.98

Variable O&M $/MWh 3.90

Heat Rate, HHV kJ/kWh 9,727

Capital $/MWh 35.04

O&M $/MWh 8.61

Fuel $/MWh 11.44

LCOE $/MWh 55.08

106Btu = 1.06 GJ

In the case of the ocean storage options, it is assumed that three IGCC power plants supply CO2

to a shoreline collection point. It was felt that this provided a more realistic evaluation for this 

option. Based on this, the ocean storage systems need to be designed to handle three times the 

quantity of CO2, i.e. 11.29 million scm (22,167 tonnes), as opposed to 3.76 million scm 

(7,389 tonnes), of CO2 per day.
xi

A pipeline specification of (152 bar, -40oC dewpoint, N2<300 ppmv, O2<40 ppmv, Ar<10 ppmv) 

was used for the quality of the CO2. The DOE/EPRI study is consistent with this except for the 

CO2 pressure. The DOE/EPRI study was based on compressing the CO2 to 83 bar. This study 

includes additional compression to 152 bar.

Methodology

Pipeline Transport

The pipeline inlet CO2 pressure is set equal to 152 bar, which is equivalent to the pressure of the 

CO2 supplied by the base case IGCC plant. Based on a recommendation that the pipeline CO2

pressure not be allowed to fall below 103 bar, this latter value is used for the pipeline outlet CO2

pressure. The maximum allowable pressure drop per unit length (?P/?L) is found as the 

difference between the pipeline inlet and outlet CO2 pressures divided by the pipeline length. 

The pipeline diameter is then calculated using the equations for pressure drop and head loss due 

to frictional resistance in a pipe, assuming turbulent flow.

Land construction cost data for natural gas pipelines was used to estimate construction costs for 

CO2 pipelines. The cost data found for natural gas pipelines consists of cost estimates filed with 

the United States’ Federal Energy Regulatory Commission (FERC), and reported in the Oil and 

Gas Journal. A regression analysis on this data yields a pipeline construction cost of 

$20,989/in/km ($33,853/in/mile). O&M costs are estimated to be $3,100/km ($5,000/mile), 

independent of pipeline diameter.

The total annual cost per tonne of CO2 is found by annualizing the construction cost using a 

capital charge rate of 15 percent per year and adding this to the annual O&M cost. Figure ES-1 

shows the cost per tonne of CO2 per 100 km as a function of CO2 mass flow rate. Economies of 

scale are reached with annual CO2 flow rates in excess of 10 million tonnes per year. At these 

rates, transport costs are less than $1 per tonne of CO2 per 100 km. Note that the annual flows 

evaluated in this study, corresponding to the IGCC plant described above, are 2.16 million 

tonnes per year (7,389 tonnes per day with an 80% capacity factor).
xii

Total Annual Cost

Construction and O&M

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

0 10 20 30 40 50 60

Mass flow rate (Mt CO2/yr)

Cost ($/t CO2/100km)

Figure ES-1

Cost for CO2 transport via pipeline as a function of CO2 mass flow rate

Note that these costs are not on a CO2 equivalent life cycle greenhouse gas basis. This 

information was developed using an MIT pipeline model. Transport values used for the rest of 

the transport and storage cases are on an equivalent life cycle greenhouse gas basis.

Enhanced Oil Recovery

For specific projects, the complex interactions between the injected CO2 and reservoir oil would 

be modeled to assess the likely performance of a proposed CO2-EOR project. Based on the 

output of this modeling, the cost of the proposed CO2 flood is calculated. However, for our 

purposes of developing general costing algorithms, ‘rules of thumb’ are used to define the 

engineering parameters needed to estimate the cost of a CO2-EOR project. These ‘rules of 

thumb’ have been derived based on information from experts in the field and the literature.

The method used for costing the EOR process can be split up into a number of steps. The 

illustration presented here uses numbers from our base case (see Table ES-3). First, the average 

amount of enhanced oil produced per day for the given CO2 mass flow rate is determined using a 

CO2 effectiveness factor of 170 standard cubic meters (6,000 standard cubic feet) of new CO2

per bbl of enhanced oil. Second, the number of production wells is found by dividing this total 

amount of enhanced oil produced per day by an assumed average of 40 bbl of enhanced oil per 

day being produced at each well. Third, a ratio of producers to injectors of 1 to 1.1 is used to 

calculate the number of injection wells from the number of production wells. Fourth, the capital 

cost of the CO2 recycle plant is determined based on a maximum CO2 recycle ratio of 3, with an 

average recyc le ratio of 1.1 being used for the plant’s O&M costs. Finally, the capital and O&M 

costs associated with the wells and the field equipment are calculated.
xiii

The EIA ‘Costs and Indices for Domestic Oil and Gas Field Equipment and Production 

Operations’2 report was used as the basis for field equipment and production operations costs. 

Costs and indices for additional secondary oil recovery equipment and its operation were 

provided for a representative lease, located in west Texas. This lease, or module, comprises 10 

production wells, 11 water injection wells and 1 disposal well, and the wells are nominally 

4,000 feet, or 1,219 m, deep.

Table ES-3 below defines three cases, a base case, a high cost case, and a low cost case. We 

analyzed typical EOR operating data to determine a base case and range for each critical 

variable. These values were then used to define the cases described in Table ES-3. Costs for 

EOR and the other CO2 storage options assessed in this project were calculated on a CO2

equivalent life-cycle (LC) GHG avoided basis as described in Chapter 2.

Table ES-3

EOR case descriptions and costing results

Parameter Units

EOR

Base Case

EOR

High Cost Case

EOR

Low Cost 

Case

CO2 Effectiveness scm/bbl enhanced oil 170 227 85

Oil Production per 

Well

bbl enhanced oil/d/well 40 20 70

Maximum Recycle 

Ratio

3 4 1

Oil Price $/bbl 15 12 20

Depth m 1,219 2,438 610

Pipeline Distance km 100 300 0

Previous 

Waterflooding

Yes No Yes

Total Oil Production* bbl enhanced oil/d 22,142 16,582 44,285

Number of 10/11 Well 

Modules*

56 83 64

New CO2* scm/day/module 68,000 45,000 59,000

Maximum Recycled 

CO2*

scm/day/module 204,000 182,000 59,000

Levelized Annual CO2

Net Storage Cost*

$/tonne CO2 eq. LC 

GHG avoided

(12.21) 73.84 (91.26)

* calculated

 

2

 Energy Information Administration (Office of Oil and Gas), “Costs and Indices for Domestic Oil and Gas Field 

Equipment and Production Operations,” [Online document], Mar. 2000, [cited Jan. 2002], Available HTTP:

http://www.eia.doe.gov/oil_gas/natural_gas/data_publications/cost_indices/c_i.html
xiv

Enhanced Coalbed Methane Recovery

As in the case of the CO2-EOR concept design, ‘rules of thumb’ are used to define the 

engineering parameters needed to estimate the cost of a CO2-ECBMR project. The illustration 

presented here uses numbers from our base case (see Table ES-4). First, the total amount of 

enhanced CBM produced per day for the given CO2 mass flow rate is determined using a CO2

effectiveness factor of two scm CO2 per scm of enhanced CBM. Second, the number of 

production wells is found by dividing this total amount of enhanced CBM produced per day by 

an assumed 14,000 scm of enhanced CBM per day being produced at each well. Third, a ratio of 

producers to injectors of 1 to 1 is used to calculate the number of injection wells from the 

number of production wells. Fourth, it is assumed that no recycling of CO2 is required. Finally, 

the cost of drilling and equipping the required production and injection wells is calculated.

Prior to acquiring a lease position, geological expenditures, geophysical expenditures, and 

engineering-based feasibility studies are often conducted. In addition, outlays are generally 

required for obtaining the lease and its associated permits. These front-end costs will vary 

greatly but may range from $20,000 to $30,000 per well for a commercial project. For this 

study, a cost of $25,000 per well is assumed.

All of the other field costs, except for the well drilling cost, are based on data contained in the 

EIA ‘Costs and Indices for Domestic Oil and Gas Field Equipment and Production Operations’ 

report. A representative ECBMR lease, or module, comprising 10 CO2 injection wells and 

10 producing wells with dewatering facilities is used for the design basis. The 10 CO2 injection 

wells are drilled to a depth of 610 m and equipped with a battery of lease equipment, which 

includes distribution lines, headers, electrical service, and controls. The 10 producing wells, also 

drilled to a depth of 610 m, are equipped with beam balanced/sucker rod dewatering.

The well drilling cost is calculated based on a relationship derived from data contained in the 

‘1998 Joint Association Survey (JAS) on Drilling Costs’ report.3 This relationship between well 

depth and drilling cost is shown in Figure ES-2. To determine the relationship, regression 

analysis was performed on drilling cost data for onshore gas and oil wells. The total well drilling 

cost is found by multiplying the cost of drilling a single well for the given reservoir depth, taken 

from the graph, by the required number of wells.

 

3

 American Petroleum Institute – Policy Analysis and Statistics Department, “1998 Joint Association Survey on 

Drilling Costs,” [Online document], Nov. 1999, Available HTTP: 

http://www.api.org/axs-api/products/joint.htm
xv

Well Drilling Cost as a Function of Depth

1998 Onshore Gas & Oil Well Data

y = 0.0888e0.0008x

0.10

1.00

10.00

0 1000 2000 3000 4000 5000 6000

Depth (m)

Cost ($M)

Figure ES-2

Well drilling cost as a function of depth

Table ES-4 defines a base case, a high cost case, and a low cost case derived from an analysis of 

typical ECBMR operating data.

Table ES-4

ECBMR case descriptions and costing results

Parameter Units

ECBMR

Base Case

ECBMR

High Cost 

Case

ECBMR

Low Cost 

Case

CO2 Effectiveness scm/scm enhanced 

CBM

2 10 1.5

CBM Production per 

Well

scm enhanced 

CBM/d/well

14,000 3,000 30,000

Gas Price $/106 Btu 2 1.80 3

Depth m 610 1,219 610

Pipeline Distance km 100 300 0

Total CBM Production* million scm enhanced 

CBM/d

1.88 0.38 2.51

Number of 10/10 Well 

Modules*

135 126 84

Number of CO2 Wells* 135 126 84

New CO2* scm/d/well 28,000 30,000 45,000

Levelized Annual CO2

Net Storage Cost*

$/tonne CO2 eq. LC 

GHG avoided

(5.59) 18.88 (25.72)

* calculated
xvi

Depleted Gas and Oil Reservoir/Aquifer Storage

Depleted natural gas and oil reservoirs, and deep saline aquifers, differ quite substantially from 

one another in terms of typical values of reservoir parameters such as pressure, thickness, depth, 

and permeability. The processes that govern the rate at which CO2 can be injected at a well, and 

thus the number of wells required, are however essentially identical for the three types of 

reservoir. Given this, the same costing methodology is applied to each of the three geologic CO2

storage options.

The cost model for the geologic CO2 storage options can be broken down into a number of 

components. First, there is a relationship for calculating the number of wells required for a given 

CO2 flow rate, CO2 downhole injection pressure, and set of reservoir parameters. Second, an 

iterative procedure is used to take into account the interdependent relationship between CO2

downhole injection pressure and well number. Third, a set of capital and O&M cost factors are 

used to determine cost based on well number.

The well number calculation requires inputs for CO2 mass flow rate, CO2 downhole injection 

pressure, and reservoir pressure, thickness, depth, and permeability. The relationship shown in 

Figure ES-3 is used to determine CO2 injectivity from CO2 mobility4. CO2 injectivity is defined 

as the mass flow rate of CO2 (m) that can be injected per unit of reservoir thickness (h) and per 

unit of downhole pressure difference (Pinj – Pres). CO2 mobility is defined as the CO2 absolute 

permeability divided by the CO2 viscosity. Given the CO2 injectivity, the CO2 injection rate per 

well (Q) can be calculated as 

Q = CO2 injectivity x h x (Pinj – Pres)

Finally, the number of wells required (n) is given by

n = m/Q

 

4

 Law, D. and S. Bachu, “Hydrogeological and numerical analysis of CO2 disposal in deep aquifers in the Alberta 

sedimentary basin,” Energy Convers. Mgmt., 37:6-8, pp. 1167-1174, 1996.
xvii

CO2 Injectivity as a Function of CO2 Mobility

y = 0.0208x

0.10

1.00

10.00

100.00

10 100 1000 10000

CO2 mobility (md/mPa.s)

CO2 injectivity (t/d/m/MPa)

Figure ES-3

CO2 injectivity as a function of CO2 mobility

The capital cost for site screening and evaluation is based on an estimate given in a recent study 

by the Battelle Memorial Institute.5 This study estimated the costs for preliminary site screening 

and candidate evaluation at $1,685,000.

All of the other costs, except for the well drilling cost, are calculated based on values given in the 

EIA ‘Costs and Indices for Domestic Oil and Gas Field Equipment and Production Operations’ 

report. Average lease equipment costs and O&M costs were developed on a per well basis. In 

the case of the injection equipment and surface maintenance, these average cost values are 

adjusted to take into account the number of wells. Similarly, the average cost value for 

subsurface maintenance is adjusted to take into account the well depth. These capital and O&M 

cost factors/functions are given in Table ES-5.

 

5

Smith, L.A. et al, “Engineering and Economic Assessment of Carbon Dioxide Sequestration in Saline 

Formations,” presented at the First National Conference on Carbon Sequestration, May 14-17, 2001, Washington 

D.C.
xviii

Table ES-5

Capital and O&M cost estimation factors/functions

Parameter Unit Value

CAPITAL COSTS

Injection Equipment 

(Flowlines & Connections)

$/well 43,600*(7,389/(280*Number_of_wells))^0.5

O&M COSTS

Normal Daily Expenses $/well 6,700

Consumables $/well 17,900

Surface Maintenance 

(Repair & Services)

$/well 13,600*(7,389/(280*Number_of_wells))^0.5

Subsurface Maintenance 

(Repair & Services)

$/well 5,000*Well_depth/1219

The well drilling cost is calculated based on the relationship derived from data contained in the 

‘1998 Joint Association Survey (JAS) on Drilling Costs’ report.

Table ES-6, Table ES-7, and Table ES-8 define a base case, a high cost case, and a low cost case 

derived from an analysis of typical data for depleted gas reservoirs, depleted oil reservoirs, and 

deep, brine aquifers, respectively.

Table ES-6

Depleted Gas Reservoir Case Descriptions and Costing Results

Parameter Units

Gas 

Reservoir

Base Case

Gas 

Reservoir

High Cost 

Case

Gas 

Reservoir

Low Cost 

Case

Pressure MPa 3.5 6.9 2.1

Thickness m 31 15 61

Depth m 1,524 3,048 610

Permeability md 1 0.8 10

Pipeline Distance km 100 300 0

Injection Rate per Well* t/d 156 57 2,975

Number of Wells* 48 129 3

Levelized Annual CO2

Storage Cost*

$/tonne CO2 eq. LC 

GHG avoided

4.87 19.43 1.20

* calculated
xix

Table ES-7

Depleted oil reservoir case descriptions and costing results

Parameter Units

Oil Reservoir

Base Case

Oil Reservoir

High Cost 

Case

Oil Reservoir

Low Cost 

Case

Pressure MPa 13.8 20.7 3.5

Thickness m 43 21 61

Depth m 1,554 2,134 1,524

Permeability md 5 5 19

Pipeline Distance km 100 300 0

Injection Rate per Well* t/d 360 115 5,690

Number of Wells* 21 65 2

Levelized Annual CO2

Storage Cost*

$/tonne CO2 eq. LC 

GHG avoided

3.82 11.16 1.21

* calculated

Table ES-8

Deep, brine aquifer case descriptions and costing results

Parameter Units

Aquifer

Base Case

Aquifer

High Cost 

Case

Aquifer

Low Cost 

Case

Pressure MPa 8.4 11.8 5.0

Thickness m 171 42 703

Depth m 1,239 1,784 694

Permeability md 22 0.8 585

Pipeline Distance km 100 300 0

Injection Rate per Well* t/d 9,363 82 889,495

Number of Wells* 1 91 1

Levelized Annual CO2

Storage Cost* 

$/tonne CO2 eq. LC 

GHG avoided

2.93 11.71 1.14

* calculated

Ocean Via Pipeline

The ocean pipeline storage option assumes that three IGCC power plants supply CO2 to a 

shoreline collection point. The CO2 is then transported via a subsea pipeline from the shoreline 

to a depth of 2,000 m, at which depth the CO2 is discharged into the deep ocean via a diffuser 

unit. The method used for calculating the cost of this process can be broken down into two steps. 

First, the diameter of the subsea pipeline is determined. It is then possible, as a second step, to 

calculate the capital and O&M costs as well as the cost per tonne of CO2.
xx

The pipeline diameter is calculated using the same method as is used in the CO2 overland 

pipeline transport model. The only difference is the means by which the maximum allowable 

pressure drop per unit length (?P/?L) is determined. In the case of CO2 overland pipeline 

transport, the pressure drop per unit length is simply found as the difference between the pipeline 

CO2 inlet and outlet pressures divided by the pipeline length. The pipeline ocean CO2 storage 

model however requires that the pressure drop per unit length calculation also take into account 

the gravity head gain and diffuser head loss. In addition, it is necessary that the CO2 be 

discharged at a pressure equal to the hydrostatic pressure.

The cost of the subsea pipeline has been determined based on cost information contained in 

McDermott’s phase II final report on ‘Large-scale CO2 Transportation and Deep Ocean 

Sequestration.’6 The capital cost of an injector unit, based on an estimate given in an IEA 

report,7 is taken to be $14.5 million. The results are shown in Table ES-9.

Table ES-9

Ocean pipeline storage case descriptions and costing results

Parameter Units

Ocean 

Pipeline

Base Case

Ocean 

Pipeline

High Cost 

Case

Ocean 

Pipeline

Low Cost 

Case

Pipeline Distance km 100 300 0

Offshore Distance km 100 300 50

Pressure Drop per Unit 

Length*

Pa/m 126 42 251

Pipe Diameter* inches 14.2 17.5 12.4

Nominal Pipe Size* inches 16 20 14

Levelized Annual CO2

Storage Cost*

$/tonne CO2 eq. LC 

GHG avoided

5.53 14.23 2.90

* calculated, 1 inch = 0.0254 m

Ocean Via Tanker

The storage option has been modeled based on design and cost information obtained from 

McDermott’s Phase I and Phase II8 final reports on ‘Large-scale CO2 Transportation and Deep 

Ocean Sequestration’ as well as e-mail communications with the reports’ author. The method 

 

6

 Sarv, H., “Large-scale CO2 transportation and deep ocean sequestration – Phase II final report,” McDermott 

Technology Inc., Ohio. Tech. Rep. DE-AC26-98FT40412, 2001.

7

 Ormerod, B., “The disposal of carbon dioxide from fossil fuel fired power stations,” IEA Greenhouse R&D 

Programme, Cheltenham. Tech. Rep. IEAGHG/SR3, Jun. 1994.

8

 Sarv, H., “Large-scale CO2 transportation and deep ocean sequestration,” McDermott Technology Inc., Ohio. 

Tech. Rep. DE-AC26-98FT40412, Mar. 1999.
xxi

used for a conceptual design of this process can be broken down into a number of steps. First, 

the number of tankers required to transport the CO2 to the offshore platform is determined. 

Second, the diameter of the vertical pipe to carry the CO2 from the platform to the injection 

depth is calculated. Third, the amount of CO2 emitted by the tankers travelling to and from the 

offshore storage site, and emitted due to boil off, is found. It is then possible, as a final step, to 

calculate the capital cost of the tankers, port facility, offshore floating platform, and vertical pipe, 

and the non-fuel and fuel O&M costs as well as the cost per tonne of CO2.

The total capital cost of the tanker ocean CO2 storage option comprises the capital cost of the 

three required tankers, the offshore floating platform, the port facility, and a 2,000-m long, 

8-inch diameter vertical pipe. The total O&M cost is calculated as the sum of the non-fuel and 

fuel O&M costs. From e-mail communications with Hamid Sarv of McDermott Technology, 

Inc., it was learned that the total annual O&M cost in the case studies was taken as the sum of 

5.6 percent and 0.02 percent of the total tanker and non-tanker capital costs, respectively, where 

the fuel cost comprised 16.5 percent of the tanker O&M cost. The non-fuel O&M cost is 

calculated in the model as 4.7 percent of the total tanker capital cost, thus excluding the fuel cost, 

plus 0.02 percent of the total non-tanker capital costs. The fuel O&M cost is determined as the 

product of the total annual fuel usage, found from multiplying the tanker fuel usage by the total 

annual distance traveled, and a diesel fuel price of $0.566/gal.

The capital and O&M cost estimation factors are summarized in Table ES-10. Table ES-11 

defines a base case, a high cost case, and a low cost case for the ocean tanker storage option.
xxii

Table ES-10

Capital and O&M cost estimation factors for ocean tanker storage

Parameter Unit Value

CAPITAL COSTS

Tanker $/tanker 55,263,000

Offshore Platform $ 200,000,000

Onshore Port Facility $ 50,000,000

Vertical Pipeline:

Construction $/in/km 351,445

Towing to Offshore 

Structure

$ 300,000

Upending, Securing & 

Anchoring

$ 3,000,000

General Facilities, 

Engineering, Permitting etc.

$ 0.3*(Tanker_capital_cost +

Offshore_platform_capital_cost +

Onshore_port_facility_capital_cost +

Vertical_pipeline_capital_cost)

O&M COSTS

Non-fuel $/y (Tanker_capital_cost*0.047) +

((Offshore_platform_capital_cost +

Onshore_port_facility_capital_cost +

Vertical_pipeline_capital_cost)*0.02) 

Fuel $/gal 0.566

1 inch = 0.0254 m, 1 gal = 3.79 liters

Table ES-11

Ocean tanker storage case descriptions and costing results

Parameter Units

Ocean Tanker

Base Case

Ocean Tanker

High Cost 

Case

Ocean Tanker

Low Cost 

Case

Pipeline Distance km 100 300 0

Offshore Distance km 100 300 50

Boil Off %/d 1 2 0.5

Diesel Price $/gal 0.566 0.8 0.45

Number of Tankers* 3 3 3

Total Annual Fuel 

Usage*

gal/y 249,001 747,004 124,501

CO2 Emitted by 

Tankers*

t/y 2,395 7,186 1,198

CO2 Emitted by Boil Off* t/y 53,362 139,415 24,638

Levelized Annual CO2

Storage Cost*

$/tonne CO2 eq. LC 

GHG avoided

17.64 22.79 15.76

* calculated

1 gal = 3.79 liters
xxiii

Summary of Results

Figure ES-4 and Figure ES-5 summarize the results for the cost of the various carbon storage 

technologies analyzed in this study on a life-cycle greenhouse gas avoided basis. Figure ES-4 

includes all the direct storage technologies, while Figure ES-5 expands the scale for storage 

technologies with no commercial by-products. The points on the graphs are for the base case 

conditions, while the bars represent the range between the high and low cost cases as outlined in 

the Tables above.

Several observations about these results are offered below:

• Excluding the more expensive ocean tanker option, the typical base case costs for CO2

storage (transport + injection) without oil or gas by-product credit is in the range of $3-5.50 

per tonne CO2 ($11-20 per tonne C). The cost range can be characterized as $2-15 per tonne 

CO2 ($7-55 per tonne C).

• With a by-product credit for the gas or oil, the credit will offset the storage costs in many 

instances. For example, in the base EOR case, one can afford to pay $12.21 per tonne of CO2

and still breakeven (i.e., the costs equal the by-product credit).

• With an oil or gas by-product, the net costs have a large range. The parameters most 

responsible for this variability are the by-product (i.e., the gas or oil) price and the ratio of 

CO2 stored to the oil or gas produced. With more oil or gas produced per unit of CO2 stored, 

the lower net CO2 storage cost, but the less CO2 stored.

Finally, Table ES-12 combines the capture and compression costs from our base IGCC plant 

with the transport and injection costs to give a total cost of sequestration on an NPV basis. 

These costs can be compared with costs of CO2 sink enhancement via forestry and reducing 

tillage on cropland.
xxiv

-12.21

-5.59

4.87 3.82 2.93 5.53

17.64

(100)

(80)

(60)

(40)

(20)

0

20

40

60

80

100

EOR ECBMR Depleted

Gas

Depleted Oil Aquifer Ocean

Pipeline

Ocean

Tanker

$/t CO2 eq. LC GHG avoided 

Figure ES-4

Levelized annual cost comparison of carbon sequestration technologies

4.87

3.82

2.93

5.53

17.64

0

5

10

15

20

25

Depleted Gas Depleted Oil Aquifer Ocean Pipeline Ocean Tanker

$/t CO2 eq. LC GHG avoided 

Figure ES-5

Levelized annual cost comparison of carbon sequestration technologies
xxv

Table ES-12

CO2 Capture + Net Storage Costs: Base Cases, NPV Basis

CO2 Storage Process LC GHG avoided

$/tonne CO2 eq. $/tonne C eq.

Enhanced Oil Recovery 4 15

Enhanced Coalbed Methane 

Recovery

11 41

Depleted Gas Reservoir 23 86

Depleted Oil Reservoir 22 81

Deep Saline Aquifer 21 77

Ocean Pipeline 24 89

Ocean Tanker 39 143

CO2 Sink Enhancement via Improved Forest Management

Basis and Approach

Costs of the CO2 sink enhancement via improved forest management were estimated from an 

energy company perspective (i.e., the amount an energy company would have to pay forest 

product companies to manage their systems to sequester additional CO2 to serve as CO2 offsets

for the company). From an energy company perspective, the cost of GHG emission offsets 

($/tonne C equivalent LC GHG avoided) from forestry options depends on the following: 

(1) levels of CO2 sink enhancement, (2) changes in GHG emissions other than from CO2 sink 

enhancement (e.g., from methane and nitrous oxide emissions), (3) reimbursement of forest 

management companies for net costs of changes in forest land area and/or management, 

(4) transaction costs for aggregating and brokering GHG credits, and (5) monitoring costs for 

assuring that contractual obligations are fulfilled.

The same financial parameters were used for forestry options as for CO2 capture and storage 

options included in this project. The rationale is that we assume that funding for both types of 

projects would come from an energy company seeking to receive credits for reducing GHG 

emissions. An energy company likely would use the same financial parameters for evaluating 

GHG abatement investments regardless of whether the investments involve CO2 capture and 

storage or CO2 sink enhancement. Costs of GHG mitigation ($/tonne C equivalent LC 

(life-cycle) GHG avoided) were calculated on an NPV basis with a 100-year planning horizon as 

described in detail in Chapter 2 and can be compared on an NPV basis with other CO2 storage 

and sink enhancement options that differ in timing of costs and timing of avoidance of GHG 

emissions.

Additional C can be sequestered in forests by establishing new plantations, restoring existing 

forests, or by avoiding deforestation. Cases representing a wide range of management types, 

trees, and geographic locations were included in this study (Table ES-13). All cases were 
xxvi

assessed assuming medium productivity levels. Where data were available, cases were also 

assessed, assuming low and high productivity levels.

Table ES-13

Forestry case studies

Type of Management Type of Trees Country/region

Plantation Loblolly pine USA (South)

Plantation Douglas Fir USA (Pacific NW)

Plantation Spanish Cedar Mexico

Restoration Pine-oak Mexico

Restoration Miombo Southern Africa

Agro-forestry Mango-Tamarind India (South)

Avoidance of deforestation Various Mexico

Summary of Costs

Costs ($/tonne C equivalent) are presented in Figure ES-6 for medium productivity on an aboveground basis (costs/above-ground C) and a C equivalent life-cycle (LC) GHG avoided basis with 

product revenues (net costs after product revenues/aboveground C + below ground C + product 

C + non-CO2 GHG C equivalent). These two accounting bases bracket the costs ($/Mg C 

equivalent) for each of the cases. The former accounting basis has most commonly been used in 

the past in order simplify monitoring and verification. The latter accounting basis is comparable 

with the basis used for CO2 storage options with revenues (e.g., enhanced oil recovery) and was 

used to calculate base-case costs. Base-case costs also assumed medium productivity. The 

Mango-Tamarind costs are relatively high on an aboveground basis because costs for the agoforestry system are high and no credit is taken for the relatively high value agricultural products. 

The Mango-Tamarind costs are relatively low on the aboveground C + below ground C + 

product C + non-CO2 GHG C equivalent basis because credit is taken for both more C and 

products that more than offset costs.
xxvii

-200

-150

-100

-50

0

50

100

150

200

US Pine US Fir Cedar PineOakMiombo Mango Deforest

$/tonne Carbon equivalent

Aboveground

Aboveground + Below ground + products - non CO2 GHGs

Figure ES-6

Forestry costs for medium productivity levels on two accounting bases

Costs are summarized in Table ES-14 for three productivity levels on both a CO2 equivalent 

LC GHG avoided and a C equivalent LC GHG avoided basis.

Table ES-14

Forestry costs for three productivity levels and CO2 and C bases

Case

Base Case -

Medium

Productivity

Low Cost 

Case-High

Productivity

High Cost 

Case-Low

Productivity

Base Case -

Medium

Productivity

Low Cost 

Case-High

Productivity

High Cost 

Case-Low

Productivity

$/tonne CO2 $/tonne CO2 $/tonne CO2 $/tonne C $/tonne C $/tonne C

US Pine 15 11 54 40 

USFir 2 1 7 3 

Cedar (15) (14) (11) (56) (51) (39)

Pine-Oak 1 (1) 2 2 (3) 6 

Miombo (24) (87)

Mango (43) (158)

Deforest 3 10 
xxviii

CO2 Sink Enhancement via Reducing Tillage on U.S. Cropland

Basis and Approach

Reducing tillage on cropland slows the rate of organic matter decomposition and increases soil 

organic matter levels until a new equilibrium level is attained (typically about 20 to 30 years 

after shifting from intensive tillage to no tillage). Carbon is sequestered in the added soil organic 

matter, resulting in an increase in soil organic carbon (SOC). Reducing tillage reduces 

equipment and fuel use, increases herbicide use, and can affect the amount of nitrogen fertilizer 

required and N2O emissions from the soil; these factors combined with the added carbon 

sequestered in soil organic matter determine the life-cycle (LC) GHG emissions avoided by 

reducing tillage. Effects of reducing tillage on equipment, fuel, herbicide, and nitrogen (N) 

fertilizer use and on N2O emissions continue as long as the switch to reduced-tillage continues. 

Costs to an energy company are an adoption incentive to get farmers to switch from intensive 

tillage to moderate- or no-tillage, transaction costs for aggregating and brokering GHG credits, 

and monitoring costs for assuring that contractual obligations are met. Costs ($/hectare) divided 

by the tonnes C equivalent (CE) LC GHG avoided/hectare give the cost in $/tonne CE LC GHG 

avoided.

The same financial parameters were used for cropland options as for CO2 capture and storage 

options included in this project. The rationale is that we assume that funding for both types of 

projects would come from an energy company seeking to receive credits for reducing GHG 

emissions. An energy company likely would use the same financial parameters for evaluating 

GHG abatement investments regardless of whether the investments involve CO2 capture and 

storage or CO2 sink enhancement. Costs of GHG mitigation ($/tonne C equivalent LC (lifecycle) GHG avoided) were calculated on an NPV basis with a 100-year planning horizon as 

described in detail in Chapter 2 and can be compared on an NPV basis with other CO2 storage 

and sink enhancement options that differ in timing of costs and timing of avoidance of GHG 

emissions.

The following regional cropland cases were included in this study, the goal being to both 

(1) include cases representing the greatest amount of avoided GHG emissions that could be 

achieved by reducing tillage and (2) illustrate the range of amounts per hectare and costs of 

avoided GHG emissions that could be achieved by reducing tillage.

1. Chapter 19: Corn/soybean rotations in the Corn Belt being converted from intensive-till to 

either no-till or moderate-till. This case includes best-case and worst-case scenarios 

concerning effects of reducing tillage on N fertilizer requirements and on N2O emissions.

2. Chapter 20: Continuous corn cropping systems in the Corn Belt being converted from 

intensive-till to either no-till or moderate-till. This case includes best-case and worst-case 

scenarios concerning effects of reducing tillage on N fertilizer requirements and on N2O 

emissions.

3. Chapter 21: Wheat/fallow rotations in the western Great Plains being converted from 

intensive-till to no-till. This case includes variations in the amount of SOC sequestered and 

amount of N2O emissions due to reducing tillage.
xxix

4. Chapter 22: Intensive-till cotton to no-till cotton in the southeastern U.S. This case includes 

variations in the amount of soil SOC sequestered and amount of N2O emissions due to 

reducing tillage. 

Summary of Costs

Base-case costs of avoided CO2 are presented in Table ES-14 for the four regional cropping 

systems included in this study, assuming that an annual adoption incentive is paid for 5, 10, 15, 

or 20 years after switching to no-till. These costs are based on the assumption that, due to soil 

quality and crop yield benefits that develop over time, a farmer would continue the no-till 

practice after the adoption incentive stops.

Table ES-14

Base-case costs of CO2 sink enhancement on U.S. cropland: intensive-till to no- till

Corn/soybean Continuous corn Wheat/fallow Cotton

Incentive 

period, years

Cost (NPV basis, 100-year planning horizon)

$/tonne CE LC GHG avoided

5 30 (8) 1/ 30 (8) 32 (9) 54 (15)

10 48 (13) 51 (14) 49 (13) 88 (24)

15 62 (17) 66 (18) 61 (17) 113 (31)

20 72 (20) 77 (21) 71 (19) 132 (36)

1/

 Values in parenthesis are $/tonne CO2 equivalent LC GHG avoided

Inputs used to estimate the base-case costs of CO2 sink enhancement via reducing tillage on U.S. 

cropland are presented in Table ES-15. Costs in Table ES-15 assume that an adoption incentive 

is paid for 20 years.

Costs of CO2 abatement are similar for corn/soybean rotations and continuous corn because of 

two offsetting factors. Relative to corn/soybean rotations, switching to no-till continuous corn 

sequesters nearly twice as much additional carbon in soil organic matter but also requires twice 

as large an adoption incentive. With continuous corn, more carbon is sequestered in SOC mainly 

because more crop residues are produced and returned to the soil and a larger adoption incentive 

is required mainly because corn is less well adapted to no-till than are soybeans.

Costs of CO2 abatement are similar for corn/soybean rotations and wheat/fallow because of 

offsetting factors. Relative to corn/soybean rotations, switching to no-till wheat/fallow 

sequesters less additional carbon in soil organic matter but also requires a smaller adoption 

incentive and results in a decrease rather than in increase in N2O emissions.
xxx

Table ES-15

Base-case inputs and costs of CO2 sink enhancement on U.S. cropland: intensive-till to 

no-till

Region

Corn 

Belt Corn Belt

 Western 

Great 

Plains

SE 

U.S.

Cropping system

Corn/

soybean

Continuous

corn

Wheat/

fallow Cotton

Quadratic SOC response curve

 ΔSOCs, kg C/ha 7221 13104

 E (slope factor) 53.1 92.0

 kg SOC/ha/y 200 300

 Years to new steady state (Ys) 27 28 20 20

 Average annual increase in SOC (years 1 to Ys), kg C/ha/y

267 468 200 300

GHG emissions from tillage-system inputs and N2O (years 1-100), kg CE/ha/y:

 Δ Fuel use -22.3 -22.9 -10.3 -23.8

 Δ Machinery maint., trans., and repair -4 -4 -1.9 -4.3

 Δ N fertilizer use 0 0 0 0

 Δ Herbicide use 3 4 11.7 0

 Δ Total GHG emissions from inputs -23.3 -22.7 -0.5 -28.1

 Δ N2O emissions from soil 30.3 30.3 -25.3 63.2

 Δ Total GHG emissions 7.0 7.6 -25.8 35.1

Average LC GHG avoided (kg CE/ha/y)

 Years 1 to Ys 260 460 226 265

 Years Ys to 100 -7 -8 26 --35

Total LC GHG avoided (years 1-100), 

tonne CE/ha 6.5 12.3 6.6 1.0

NPV Total LC GHG avoided (years 1-100),

tonne CE/ha 4.3 7.7 2.7 2.3

Costs, $/ha/y

 Transaction (year 1) 5 5 5 5

 Adoption incentive (years 1-20) 25 50 15 25

 Monitoring (years 1-100) 1 1 1 1

Total costs (years 1-100), $/ha 605 1105 405 605

NPV Total costs (years 1-100), $/ha 306 590 195 306

Cost (NPV basis), $/tonne CE LC GHG 

avoided 72 77 71 132

Cost (NPV basis), $/tonne CO2 equivalent 

LC GHG avoided 20 21 19 36

With wheat/fallow, less carbon is sequestered in SOC mainly because less crop residue is 

produced and returned to the soil on an annual basis and a smaller adoption incentive is required 
xxxi

because increases in wheat yields partially offset higher production costs with no-till. Mainly 

because of the dry climate in which wheat/fallow systems are used, switching to no-till 

wheat/fallow reduces N2O emissions whereas switching to no-till corn/soybeans increases N2O 

emissions.

Costs of CO2 abatement are significantly higher for cotton than for corn/soybean rotations, 

mainly because switching to no-till increases N2O emissions significantly more with cotton than 

with corn/soybean rotations. The greater increase in N2O emissions with no-till cotton than with 

no-till corn/soybeans is apparently due to the warmer, wetter climate in the southeastern U.S. 

where most of the cotton is grown.

Switching from intensive-till to no-till corn/soybean rotations or to no-till continuous corn can 

result in significantly higher N fertilizer requirements and N2O emissions unless N fertilizer is 

managed efficiently. This can greatly reduce the amount of GHG emissions avoided because 

production and use of N fertilizer is very energy intensive and N2O has a radiative forcing factor 

296 times greater the CO2 on a mass basis. Effects of increases in N fertilizer rate and N2O 

emissions on LC GHG avoided and costs of GHG avoidance are discussed below.

Examples of the large effects of N rate and N2O emissions on amount of GHG emissions avoided 

and cost per tonne of GHG avoided are presented in Table ES-16. The scenarios in Table 3 are 

for switching from intensive-till to no-till in a corn/soybean rotation in the Corn Belt. No 

significant change in N2O emissions due switching from intensive-till to no-till is a possibility 

with good N management, especially on well-drained soils. The increases in N rate and N2O 

emissions in Table 3 due to switching from intensive-till to no-till are possibilities with poor 

N management. Amounts of GHG avoided range from 9.6 tonnes CE LC GHG avoided/ha 

(35.3 tonnes CO2 equivalent LC GHG avoided/ha) with a best-case N rate and N2O scenario to -

3.0 tonnes CE LC GHG avoided/ha (-11.0 tonnes CO2 equivalent LC GHG avoided/ha) for a 

worst-case scenario. Costs on an NPV basis range from $64/tonne CE LC GHG avoided 

($17/tonne CO2 equivalent LC GHG avoided) with a best-case N rate and N2O scenario to 

$113/tonne CE LC GHG avoided ($31/tonne CO2 equivalent LC GHG avoided) for a worst-case 

scenario. Differences between the best-case and worst-case scenarios regarding amount of GHG 

avoided are very large because the annual effects are manifested over the entire 100-year 

planning horizon. In order for reducing tillage on corn/soybean rotations to be a viable option 

for avoiding GHG emissions, N fertilizer will have to be managed efficiently so that the amount 

of N fertilizer used and the N2O emissions are not significantly increased. This conclusion is 

also applicable to continuous corn in the Corn Belt.
xxxii

Table ES-16

Effects of increases in N rate and N2O emissions on amounts and costs of avoided GHG 

via switching from intensive-till to no-till in a Corn/Soybean rotation

?N rate ?N2O rate Years 1-100 NPV basis, years 1-100

kg N/ha/yr kg/CE/ha/yr kg/CE/ha/yr

tonne CE LC 

GHG 

avoided/ha

tonne CE LC 

GHG 

avoided/ha

$/tonne CE 

LC GHG 

avoided1/

0 0 0 9.6 4.8 64 2/

0 0 30.3 6.5 4.3 72 3/

25 14.4 63.5 1.8 3.5 88 4/

50 28.8 96.6 -3.0 2.7 113 5/

1/

 Assumes incentive payments for 20 years.

2/

 Base case except for no change in N2O emissions due to switching to no-till.

3/

 Base case.

4/

 Base case except for an increase in N rate due to switching from to no-till.

5/

 Base case except for an additional increase in N rate due to switching from to no-till.

Summary Comparisons and Conclusions

Presented in this section are comparisons among options evaluated in this project and between 

CO2 capture, transport and storage and indirect capture by forests and cropland via reducing 

tillage.

Direct Verses Indirect Sequestration

Shown in Figure ES-7 is a comparison of all of the base case values in $/tonne of CO2 equivalent 

LC GHG avoided on a NPV basis plotted from lowest to highest cost.
xxxiii

-50

-40

-30

-20

-10

0

10

20

30

40

50

Mango

Miombo

Cedar

Pine-Oak

USFir

Deforest

EOR

ECBMR

Wheat/fallow

Corn/soybean

Continuous corn

US Pine

Aquifer

Oil Reservoir

Gas Reservoir

Ocean Pipeline

Continuous cotton

Ocean Tanker

Base Case

$/tonne of CO2 Eq. LC GHG Avoided

Figure ES-7

Comparison of direct and indirect sequestration cases.

As shown in Figure ES-7, all of the forestry options are the least expensive except for the US 

Pine case. Next are the EOR and ECBMR options followed by most of the cropland options. 

Figure ES-8 includes the available high and low cases. The main impact of these cases is on the 

cropland cases where the low cases would make them competitive with ECBMR and the high 

cases would make them about the same as the other geologic storage cases.
xxxiv

-50

-40

-30

-20

-10

0

10

20

30

40

50

Mango

Miombo

Cedar

Pine-Oak

USFir

Deforest

EOR

ECBMR

Wheat/falow

Corn/soybean

Continuous corn

US Pine

Aquifer

Oil Reservoir

Gas Reservoir

Ocean Pipeline

Continuous cotton

Ocean Tanker

$/t CO2 Eq. LC GHG Avoided

Base Case

High Case

Low Case

Figure ES-8

Comparison of direct and indirect sequestration cases including high and low cases

Base-case forest and cropland areas required to offset LC GHG emissions avoided by base-case 

CO2 capture and storage options are presented in Table ES-17. These land area equivalents are 

for a 100-year planning horizon. The carbon equivalent (CE) LC GHG emissions avoided by the 

capture and storage options assume CO2 capture and injection into storage reservoirs during the 

20-year book life of the capture and storage plants and storage of injected CO2 for another 

80 years. For the ocean storage options, the CE LC GHG avoided is reduced for leakage of CO2

throughout the 100-year storage period. Zero CO2 leakage is assumed for the other CO2 storage 

options. The CE LC GHG emissions avoided by the forest and cropland options assume that the 

improved forestry or cropland practices are maintained for 100 years. Generally, ten times or 

more cropland than forest land is required to offset the CO2 storage options.
xxxv

Table ES-17

Base-case forest and cropland areas required to offset LC GHG emissions avoided by base -case CO2 capture and storage 

options.

Aquifer Storage Enhanced Oil Recovery Depleted Oil Reservoirs Depleted Gas Reservoirs Coal Bed Methane Recovery Ocean Pipeline Storage Ocean Tanker Storage

Forestry / Cropland Forestry / Cropland Forestry / Cropland Forestry / Cropland Forestry / Cropland Forestry / Cropland Forestry / Cropland

Forestry System - Medium Productivity Area to Equal Area to Equal Area to Equal Area to Equal Area to Equal Area to Equal Area to Equal 

CO 2 Capture & Storage CO2 Capture & Storage CO2 Capture & Storage CO 2 Capture & Storage CO2 Capture & Storage CO2 Capture & Storage CO 2 Capture & Storage

 (tonne CE/ha) (Mtonne CE) (Mha) (Mtonne CE) (Mha) (Mtonne CE) (Mha) (Mtonne CE) (Mha) (Mtonne CE) (Mha) (Mtonne CE) (Mha) (Mtonne CE) (Mha)

Plantation, USA, Loblolly Pine 183.14 9.81 0.05 9.09 0.05 9.81 0.05 9.81 0.05 9.54 0.05 28.28 0.15 28.00 0.15

Plantation, USA, Douglas Fir 452.65 9.81 0.02 9.09 0.02 9.81 0.02 9.81 0.02 9.54 0.02 28.28 0.06 28.00 0.06

Plantation (Taungya), Mexico, Spanish cedar 277.40 9.81 0.04 9.09 0.03 9.81 0.04 9.81 0.04 9.54 0.03 28.28 0.10 28.00 0.10

Restoration, Mexico, Pine-oak 183.39 9.81 0.05 9.09 0.05 9.81 0.05 9.81 0.05 9.54 0.05 28.28 0.15 28.00 0.15

Restoration, Southern Africa, Miombo 59.26 9.81 0.17 9.09 0.15 9.81 0.17 9.81 0.17 9.54 0.16 28.28 0.48 28.00 0.47

Agro- forestry, Southern India, Mango- tamarind 71.53 9.81 0.14 9.09 0.13 9.81 0.14 9.81 0.14 9.54 0.13 28.28 0.40 28.00 0.39

Avoidance of deforestation, Mexico,Various 207.66 9.81 0.05 9.09 0.04 9.81 0.05 9.81 0.05 9.54 0.05 28.28 0.14 28.00 0.13

Cropland System

Two Year Corn/Soybean Rotation 6.51 9.81 1.51 9.09 1.39 9.81 1.51 9.81 1.51 9.54 1.46 28.28 4.34 28.00 4.30

( Intensive-till to No-till )

Continuous Corn 12.33 9.81 0.80 9.09 0.74 9.81 0.80 9.81 0.80 9.54 0.77 28.28 2.29 28.00 2.27

( Intensive-till to No-till )

Continuous Cotton 0.99 9.81 9.90 9.09 9.18 9.81 9.90 9.81 9.90 9.54 9.64 28.28 28.56 28.00 28.28

( Intensive-till to No-till )

Two Year Wheat / Fallow Rotation 6.58 9.81 1.49 9.09 1.38 9.81 1.49 9.81 1.49 9.54 1.45 28.28 4.30 28.00 4.26

( Intensive-till to No-till )

(Above/below ground C, non-CO2 GHGs, & timber 

products/revenues)

Equivalent Forestry / Cropland Area Required to

Offset Geologic & Ocean Options

LC Avoided GHG Basis (100 year summation)
xxxvi

Conclusions

Making these comparisons was a challenge due to the differences between direct and indirect 

cases. The resulting numbers should only be used as a relative indication of cost. Site-specific 

characteristics could significantly change the outcome of the comparisons. In this study, the 

forestry options are very attractive along with capture using EOR for storage. Other factors, 

such as land availability and storage option location and capacity will also play a significant part 

in which options make the most sense at a given site.

The current work should only be considered a starting point. Better cost information and 

improvements to processes are likely to be developed and need to be incorporated into updates of 

this information. In addition, there are plans to update and add to portions of the current work to 

answer questions that arose but were beyond the scope of the current effort. For example, what 

happens to the costs if the power plant operates for longer than the plant book life? Also, how do 

the economics look for other power generation options? These and other questions will be 

evaluated in updates to this work.
xxxvii

CONTENTS

1 INTRODUCTION...................................................................................................................... 1-1

1.1 Economic Framework....................................................................................................... 1-1

1.2 Concepts Evaluated.......................................................................................................... 1-2

1.2.1 Aquifer Storage ......................................................................................................... 1-3

1.2.2 Oil Reservoir Storage with Enhanced Oil Recovery (EOR)..................................... 1-5

1.2.3 Depleted Oil and Gas Reservoir Storage ................................................................. 1-7

1.2.4 Coal Bed Storage...................................................................................................... 1-9

1.2.5 Ocean Storage ........................................................................................................ 1-11

1.2.6 Forest Sink Enhancement....................................................................................... 1-13

1.2.7 Cropland Sink Enhancement .................................................................................. 1-15

1.3 Report Organization........................................................................................................ 1-17

1.4 References...................................................................................................................... 1-17

2 METHODOLOGIES FOR CALCULATING THE COST OF CAPTURING AND 

STORING CO2............................................................................................................................. 2-1

2.1 Revenue Requirement Methodology ................................................................................ 2-1

2.1.1 Approach for CO2 Capture........................................................................................ 2-1

2.1.2 Adjustment for CO2 Storage...................................................................................... 2-3

2.1.3 Adjustment of Levelized Carrying Charge Factor as a Function of Discount 

Rate .................................................................................................................................... 2-3

2.1.4 Impact of CO2 Storage Costs on Cost of Electricity................................................. 2-4

2.2 Capture Costs on CO2 Captured, CO2 Avoided, and Life-Cycle GHG Avoided 

Bases....................................................................................................................................... 2-5

2.2.1 CO2 Captured Basis.................................................................................................. 2-5

2.2.2 CO2 Avoided Basis.................................................................................................... 2-6

2.2.3 LC GHG Avoided Basis............................................................................................. 2-7

2.3 Combining Capture and Storage Costs............................................................................ 2-7

2.3.1 CO2 Captured Basis.................................................................................................. 2-7

2.3.2 CO2 Avoided Basis (CO2 Capture and Storage Combined)..................................... 2-8
xxxviii

2.3.2 CO2 Avoided Basis (CO2 Capture and Storage Combined)..................................... 2-9

2.3.3 GHG Avoided in CO2 Capture and Storage Processes Combined on a 

Life-Cycle (LC) Basis.......................................................................................................... 2-9

2.3.4 Costs of CO2 Storage on a Life-Cycle GHG Avoided Basis (Storage Only).......... 2-10

2.4 Comparing the Economics of CO2 Storage and Sink Enhancement Options 

Differing in Timing and Permanence of Sequestration......................................................... 2-11

2.5 References...................................................................................................................... 2-14

3 STORING CAPTURED CO2 – BASIS & APPROACH............................................................ 3-1

3.1 Technologies Evaluated.................................................................................................... 3-1

3.2 Approach........................................................................................................................... 3-1

3.3 Common Design Basis ..................................................................................................... 3-2

3.4 Greenhouse Gas Emissions Inventory............................................................................. 3-4

3.4.1 Emissions from Deep Mining .................................................................................... 3-5

3.4.2 Emissions from Rail Transport.................................................................................. 3-6

3.4.3 Emissions from the Operation of the Power Plant.................................................... 3-6

3.4.4 Emissions from the Construction and Decommissioning of the Power Plant .......... 3-6

3.4.5 Emissions from the Compression of CO2 ................................................................. 3-7

3.4.6 Emissions from the Transportation of CO2 ............................................................... 3-7

3.4.7 Emissions from the CO2 Storage Options ................................................................ 3-7

3.5 References........................................................................................................................ 3-9

4 PIPELINE TRANSPORT.......................................................................................................... 4-1

4.1 Introduction........................................................................................................................ 4-1

4.2 State of the Art .................................................................................................................. 4-1

4.3 Process Description.......................................................................................................... 4-4

4.4 Methodology Used ............................................................................................................ 4-5

4.4.1 Diameter Calculation................................................................................................. 4-5

4.4.2 Cost Calculations ...................................................................................................... 4-7

4.5 Design Basis ................................................................................................................... 4-10

4.5.1 Pipeline Design ....................................................................................................... 4-10

4.5.2 Capital and O&M Cost Inputs ................................................................................. 4-11

4.6 MIT Model Results .......................................................................................................... 4-11

4.7 Comparison to Literature ................................................................................................ 4-11

4.7.1 Studies Used in Model Evaluation .......................................................................... 4-11

4.7.2 Comparison of Values from Model and Studies ..................................................... 4-12
xxxix

4.8 References...................................................................................................................... 4-15

5 ENHANCED OIL RECOVERY................................................................................................. 5-1

5.1 Introduction........................................................................................................................ 5-1

5.2 State of the Art .................................................................................................................. 5-1

5.2.1 Applications ............................................................................................................... 5-1

5.2.2 Storage Potential....................................................................................................... 5-2

5.2.3 Storage Mechanics.................................................................................................... 5-3

5.2.4 Feasibility of Storage Option..................................................................................... 5-4

5.3 Process Description.......................................................................................................... 5-4

5.4 Methodology Used ............................................................................................................ 5-7

5.4.1 CO2 Effectiveness ..................................................................................................... 5-7

5.4.2 Rate of Enhanced Oil Production at Producer.......................................................... 5-9

5.4.3 Ratio of Producers to Injectors................................................................................ 5-10

5.4.4 CO2 Recycle Ratio .................................................................................................. 5-11

5.4.5 Reworking of Existing Wells.................................................................................... 5-11

5.4.6 Cost Calculations .................................................................................................... 5-12

5.5 Design Basis ................................................................................................................... 5-14

5.5.1 Module design ......................................................................................................... 5-14

5.5.2 Capital and O&M Cost Inputs ................................................................................. 5-17

5.6 Model Results.................................................................................................................. 5-19

5.7 Sensitivity Analysis.......................................................................................................... 5-20

5.8 Comparison to Literature ................................................................................................ 5-22

5.9 References...................................................................................................................... 5-23

6 ENHANCED COALBED METHANE ....................................................................................... 6-1

6.1 Introduction........................................................................................................................ 6-1

6.2 State of the Art .................................................................................................................. 6-1

6.2.1 Applications ............................................................................................................... 6-1

6.2.2 Storage Potential....................................................................................................... 6-1

6.2.3 Storage Mechanics.................................................................................................... 6-2

6.2.4 Feasibility of Storage Option..................................................................................... 6-2

6.3 Process Description.......................................................................................................... 6-3

6.4 Methodology Used ............................................................................................................ 6-6

6.4.1 CO2 Effectiveness ..................................................................................................... 6-6
xl

6.4.2 Rate of Enhanced CBM Production.......................................................................... 6-6

6.4.3 Ratio of Producers to Injectors.................................................................................. 6-8

6.4.4 CO2 Recycle Ratio .................................................................................................... 6-8

6.4.5 Drilling and Equipping of Production and Injection Wells......................................... 6-8

6.4.6 Cost Calculations ...................................................................................................... 6-9

6.5 Design Basis ................................................................................................................... 6-11

6.5.1 Module Design ........................................................................................................ 6-11

6.5.2 Capital and O&M Cost Inputs ................................................................................. 6-14

6.6 Model Results.................................................................................................................. 6-16

6.7 Sensitivity Analysis.......................................................................................................... 6-19

6.8 Comparison to Literature ................................................................................................ 6-20

6.9 References...................................................................................................................... 6-22

7 DEPLETED GAS AND OIL RESERVOIR/AQUIFER STORAGE........................................... 7-1

7.1 Introduction........................................................................................................................ 7-1

7.2 State of the Art .................................................................................................................. 7-1

7.2.1 Applications ............................................................................................................... 7-1

7.2.2 Storage Potential....................................................................................................... 7-1

Depleted Natural Gas Reservoirs................................................................................. 7-1

Depleted Oil Reservoirs ................................................................................................ 7-2

Deep Saline Aquifers..................................................................................................... 7-2

7.2.3 Storage Mechanics.................................................................................................... 7-3

7.2.4 Storage Option Feasibility......................................................................................... 7-3

7.3 Process Description.......................................................................................................... 7-4

7.4 Methodology Used ............................................................................................................ 7-6

7.4.1 Well Number Calculation........................................................................................... 7-7

7.4.2 CO2 Downhole Injection Pressure Calculation ......................................................... 7-8

7.4.3 Cost Calculations ...................................................................................................... 7-9

7.5 Design Basis ................................................................................................................... 7-10

7.5.1 Reservoir Parameters and Well Numbers for Base and Sensitivity Cases ........... 7-10

7.5.2 Capital and O&M Cost Inputs ................................................................................. 7-14

7.6 Model Results.................................................................................................................. 7-15

7.6.1 Depleted Gas Reservoir: Spreadsheet Orientation and Typical Costs................. 7-15

7.6.2 Depleted Oil Reservoir: Spreadsheet Orientation and Typical Costs................... 7-17

7.6.3 Aquifer Storage: Spreadsheet Orientation and Typical Costs ............................... 7-20
xli

7.7 Sensitivity Analyses ........................................................................................................ 7-21

7.7.1 Depleted Gas Reservoir.......................................................................................... 7-21

7.7.2 Depleted Oil Reservoir............................................................................................ 7-23

7.7.3 Aquifer ..................................................................................................................... 7-25

7.8 Comparison To Literature ............................................................................................... 7-27

7.8.1 Studies Used in Model Evaluation .......................................................................... 7-27

7.8.2 Comparison of Values from Model and Studies ..................................................... 7-27

7.9 References...................................................................................................................... 7-30

8 OCEAN VIA PIPELINE ............................................................................................................ 8-1

8.1 Introduction........................................................................................................................ 8-1

8.2 State of the Art .................................................................................................................. 8-1

8.3 Process Description.......................................................................................................... 8-2

8.4 Methodology Used ............................................................................................................ 8-2

8.4.1 Diameter Calculation................................................................................................. 8-3

8.4.2 Cost Calculations ...................................................................................................... 8-4

8.5 Design Basis ..................................................................................................................... 8-5

8.5.1 Pipeline Design ......................................................................................................... 8-5

8.5.2 Capital and O&M Cost Inputs ................................................................................... 8-6

8.6 Model Results.................................................................................................................... 8-6

8.7 Sensitivity Analysis............................................................................................................ 8-8

8.8 Comparison to Literature ................................................................................................ 8-10

8.8.1 Studies Used in Model Evaluation .......................................................................... 8-10

8.8.2 Comparison of Values from Model and Studies ..................................................... 8-11

8.9 References...................................................................................................................... 8-13

9 OCEAN VIA TANKER.............................................................................................................. 9-1

9.1 Introduction........................................................................................................................ 9-1

9.2 State of the Art .................................................................................................................. 9-1

9.3 Process Description.......................................................................................................... 9-1

9.4 Methodology Used ............................................................................................................ 9-1

9.4.1 Tanker Number Calculation ...................................................................................... 9-2

9.4.2 Vertical Pipe Diameter Calculation ........................................................................... 9-2

9.4.3 CO2 Emissions Calculations ..................................................................................... 9-4

9.4.4 Cost Calculations ...................................................................................................... 9-4
xlii

9.5 Design Basis ..................................................................................................................... 9-6

9.5.1 System Design.......................................................................................................... 9-6

9.5.2 Capital and O&M Cost Inputs ................................................................................... 9-7

9.6 Model Results.................................................................................................................... 9-7

9.7 Sensitivity Analysis.......................................................................................................... 9-10

9.8 Comparison to Literature ................................................................................................ 9-11

9.9 References...................................................................................................................... 9-12

10 CO2 SINK ENHANCEMENT IN FORESTS—BASIS AND APPROACH ........................... 10-1

10.1 Introduction.................................................................................................................... 10-1

10.2 Study Basis ................................................................................................................... 10-1

10.2.1 Overall Basis ......................................................................................................... 10-1

10.2.2 Carbon Sink Enhancement................................................................................... 10-2

10.2.3 Reduction in Emissions Resulting from Use of Wood and Biomass.................... 10-2

10.2.4 Non-CO2 Greenhouse Gases ............................................................................... 10-2

10.2.5 Ancillary Benefits................................................................................................... 10-2

10.2.6 Concerns and Uncertainties.................................................................................. 10-3

10.2.6.1 Additionality ................................................................................................... 10-3

10.2.6.2 Leakage......................................................................................................... 10-4

10.2.6.3 Security.......................................................................................................... 10-4

10.2.6.4 Non-Greenhouse Gas Effects....................................................................... 10-5

10.3 Sequestration Cases..................................................................................................... 10-5

10.3.1 Descriptions........................................................................................................... 10-5

10.3.2 Quantities of Carbon Sequestered ....................................................................... 10-6

10.3.2.1 Above-Ground Carbon.................................................................................. 10-7

10.3.2.2 Below-Ground Carbon .................................................................................. 10-8

10.3.2.3 Wood Products.............................................................................................. 10-8

10.3.3 Non-CO2 Greenhouse Gases ............................................................................... 10-9

10.3.3.1 Nitrous Oxide from Forests and Baseline Land Uses ................................ 10-10

10.3.3.2 Methane from Forests and Baseline Land Uses ........................................ 10-10

10.3.3.3 Net Emissions of Nitrous Oxide and Methane From Forestry.................... 10-11

10.3.3.4 Emissions From Biomass Burning.............................................................. 10-11

10.3.3.5 Other Atmospheric Emissions..................................................................... 10-12

10.3.4 Costs.................................................................................................................... 10-12

10.3.4.1 Establishment and Maintenance................................................................. 10-12
xliii

10.3.4.2 Land Costs .................................................................................................. 10-12

10.3.4.3 Timber Revenues........................................................................................ 10-13

10.3.4.4 Monitoring.................................................................................................... 10-13

10.3.4.5 Financial Parameters.................................................................................. 10-13

10.4 References.................................................................................................................. 10-14

11 CASE 1: SOUTHERN U.S. LOBLOLLY PINE PLANTATIONS......................................... 11-1

11.1 Introduction.................................................................................................................... 11-1

11.2 Ecology.......................................................................................................................... 11-1

11.3 Management Operations .............................................................................................. 11-1

11.3.1 Establishment........................................................................................................ 11-1

11.3.2 Maintenance.......................................................................................................... 11-2

11.3.3 Harvesting ............................................................................................................. 11-2

11.3.4 Re-establishment .................................................................................................. 11-2

11.3.5 Classification of Productivity ................................................................................. 11-2

11.4 Carbon Sequestration Potential.................................................................................... 11-3

11.4.1 Baseline................................................................................................................. 11-3

11.4.2 Carbon Uptake and Storage by Pinus Taeda....................................................... 11-3

11.5 Costs ............................................................................................................................. 11-3

11.5.1 Establishment and Maintenance........................................................................... 11-3

11.5.2 Land Costs ............................................................................................................ 11-4

11.5.3 Potential Income.................................................................................................... 11-4

11.6 Summary of Model Inputs and Results......................................................................... 11-4

11.7 References.................................................................................................................... 11-8

12 CASE 2: PACIFIC NORTHWEST DOUGLAS FIR PLANTATIONS.................................. 12-1

12.1 Introduction.................................................................................................................... 12-1

12.2 Ecology.......................................................................................................................... 12-1

12.3 Management Operations .............................................................................................. 12-1

12.3.1 Establishment........................................................................................................ 12-1

12.3.2 Maintenance.......................................................................................................... 12-2

12.3.3 Harvesting ............................................................................................................. 12-2

12.3.4 Re-establishment .................................................................................................. 12-2

12.4 Classification of Productivity ......................................................................................... 12-2

12.5 Carbon Sequestration Potential.................................................................................... 12-3
xliv

12.5.1 Baseline................................................................................................................. 12-3

12.5.2 Carbon Uptake and Storage by Pseduotsuga Menziesii...................................... 12-3

12.6 Costs ............................................................................................................................. 12-3

12.6.1 Establishment and Maintenance........................................................................... 12-3

12.6.2 Land....................................................................................................................... 12-3

12.6.3 Potential Income.................................................................................................... 12-4

12.7 Summary of Model Inputs and Results......................................................................... 12-4

12.8 References.................................................................................................................... 12-8

13 CASE 3: SPANISH CEDAR PLANTATIONS IN MEXICO USING THE TAUNGYA 

SYSTEM..................................................................................................................................... 13-1

13.1 Introduction.................................................................................................................... 13-1

13.2 Ecology.......................................................................................................................... 13-1

13.3 Management Operations .............................................................................................. 13-1

13.3.1 Establishment........................................................................................................ 13-2

13.3.2 Maintenance.......................................................................................................... 13-2

13.3.3 Harvesting ............................................................................................................. 13-3

13.3.4 Re-establishment .................................................................................................. 13-3

13.4 Classification of Productivity ......................................................................................... 13-3

13.5 Carbon Sequestration Potential.................................................................................... 13-3

13.5.1 Baseline................................................................................................................. 13-3

13.5.2 Carbon Uptake and Storage by Cedrela Odorata ................................................ 13-4

13.6 Costs ............................................................................................................................. 13-5

13.6.1 Establishment and Maintenance........................................................................... 13-5

13.6.2 Land Cost.............................................................................................................. 13-5

13.6.3 Potential Income.................................................................................................... 13-5

13.7 Summary of Model Inputs and Results......................................................................... 13-5

13.8 Reference.................................................................................................................... 13-10

14 CASE 4: RESTORATION OF DEGRADED PINE-OAK WOODLAND IN CHIAPAS, 

MEXICO THROUGH ENRICHMENT PLANTING WITH PINUS OOCARPA.......................... 14-1

14.1 Introduction.................................................................................................................... 14-1

14.2 Ecology1,2,3,4................................................................................................................... 14-1

14.3 Management Operations .............................................................................................. 14-2

14.3.1 Establishment........................................................................................................ 14-2

14.3.2 Maintenance.......................................................................................................... 14-2
xlv

14.3.3 Harvesting ............................................................................................................. 14-2

14.3.4 Re-establishment .................................................................................................. 14-2

14.4 Classification of Productivity ......................................................................................... 14-3

14.5 Carbon Sequestration Potential.................................................................................... 14-3

14.5.1 Baseline................................................................................................................. 14-3

14.5.2 Carbon Uptake by the Regenerating Forest......................................................... 14-4

14.6 Costs:............................................................................................................................ 14-5

14.6.1 Establishment and Maintenance........................................................................... 14-5

14.6.2 Land....................................................................................................................... 14-5

14.6.3 Potential Income.................................................................................................... 14-5

14.7 Summary of Model Inputs and Results......................................................................... 14-5

14.8 References.................................................................................................................. 14-10

15 CASE 5: RESTORATION OF DEGRADED DRY MIOMBO WOODLAND IN 

SOUTHERN AFRICA................................................................................................................ 15-1

15.1 Introduction.................................................................................................................... 15-1

15.2 Ecology.......................................................................................................................... 15-1

15.3 Management Operations .............................................................................................. 15-1

15.3.1 Establishment........................................................................................................ 15-2

15.3.2 Maintenance.......................................................................................................... 15-2

15.3.3 Harvesting ............................................................................................................. 15-2

15.3.4 Re-establishment .................................................................................................. 15-2

15.4 Classification of Productivity ......................................................................................... 15-2

15.5 Carbon Sequestration Potential.................................................................................... 15-2

15.5.1 Baseline................................................................................................................. 15-2

15.5.2 Carbon Uptake and Storage in Miombo Woodland.............................................. 15-3

15.6 Costs ............................................................................................................................. 15-3

15.6.1 Establishment and Maintenance........................................................................... 15-3

15.6.2 Land....................................................................................................................... 15-3

15.6.3 Potential Income.................................................................................................... 15-3

15.7 Additional Information ................................................................................................... 15-4

15.8 Summary of Model Inputs and Results......................................................................... 15-4

15.9 References.................................................................................................................... 15-7

16 CASE 6: AGRO-FORESTRY: MANGO AND NATIVE FRUIT ORCHARDS IN SEMIARID AREAS OF SOUTHERN INDIA...................................................................................... 16-1
xlvi

16.1 Introduction.................................................................................................................... 16-1

16.2 Ecology.......................................................................................................................... 16-1

16.3 Classification of Productivity ......................................................................................... 16-2

16.4 Management Operations .............................................................................................. 16-2

16.4.1 Establishment........................................................................................................ 16-2

16.4.2 Harvesting/Replanting........................................................................................... 16-3

16.5 Carbon Sequestration Potential.................................................................................... 16-3

16.5.1 Baseline................................................................................................................. 16-3

16.5.2 Carbon Uptake and Storage by Orchards ............................................................ 16-3

16.6 Costs ............................................................................................................................. 16-4

16.6.1 Establishment and Maintenance........................................................................... 16-4

16.6.2 Land....................................................................................................................... 16-4

16.6.3 Potential Income.................................................................................................... 16-4

16.7 Additional Comments.................................................................................................... 16-5

16.8 Summary of Model Inputs and Results......................................................................... 16-5

16.9 References / Information Sources ................................................................................ 16-8

17 CASE 7: AVOIDANCE OF DEFORESTATION IN CHIAPAS, MEXICO............................ 17-1

17.1 Introduction.................................................................................................................... 17-1

17.2 Ecology.......................................................................................................................... 17-1

17.3 Carbon Sequestration Potential.................................................................................... 17-2

17.4 Costs ............................................................................................................................. 17-7

17.4.1 Establishment and Maintenance........................................................................... 17-7

17.4.2 Land Costs ............................................................................................................ 17-7

17.4.3 Potential Income.................................................................................................... 17-8

17.5 Summary of Model Inputs and Results......................................................................... 17-8

17.6 References.................................................................................................................. 17-11

18 ECONOMICS OF CO2 SINK ENHANCEMENT VIA REDUCING TILLAGE ON U.S. 

CROPLAND—BASIS AND APPROACH................................................................................. 18-1

18.1 Introduction.................................................................................................................... 18-1

18.2 Tillage-System Definitions ............................................................................................ 18-1

18.3 Effects of Tillage-System on the Amount of Carbon Stored in Soil Organic Matter.... 18-3

18.4 Regions With Greatest Potential for Increasing Soil Organic Carbon ......................... 18-5

18.5 Current Levels of Conservation-Tillage Adoption......................................................... 18-8

18.5.1 Corn and Soybeans in the Corn Belt .................................................................... 18-8
xlvii

18.5.2 Small Grains........................................................................................................ 18-10

18.5.3 Cotton .................................................................................................................. 18-11

18.6 GHG Effects in Addition to Carbon Storage in Soil Organic Matter........................... 18-12

18.6.1 Fuel Use.............................................................................................................. 18-12

18.6.2 Machinery Required ............................................................................................ 18-13

18.6.3 N Fertilizer Use.................................................................................................... 18-13

18.6.4 Herbicide Use...................................................................................................... 18-13

18.6.5 N2O Emissions From Soil.................................................................................... 18-13

18.7 Costs of Reducing Tillage as a Means of Offsetting Utility GHG Emissions............. 18-14

18.8 Cropland Case Studies Included in This Project........................................................ 18-16

18.9 References.................................................................................................................. 18-17

19 CORN/SOYBEAN ROTATIONS IN THE U.S. CORN BELT .............................................. 19-1

19.1 Introduction.................................................................................................................... 19-1

19.2 Corn/Soybean Rotation (Base Cases)......................................................................... 19-1

19.2.1 Increases in Soil Organic Carbon......................................................................... 19-3

19.2.2 Costs...................................................................................................................... 19-5

19.2.3 GHG Effects in Addition to Carbon Storage in Soil Organic Matter..................... 19-5

19.2.3.1 Fuel Use........................................................................................................ 19-5

19.2.3.2 Machinery Manufacture, Transportation, and Repair................................... 19-6

19.2.3.3 Nitrogen Fertilizer Use.................................................................................. 19-7

19.2.3.4 Herbicide Use.............................................................................................. 19-10

19.2.3.5 N2O Emissions from Soil............................................................................. 19-10

19.2.3.6 Summary of Avoided GHG Emissions........................................................ 19-12

19.2.4 Summary............................................................................................................. 19-12

19.2.5 Sensitivity Summary............................................................................................ 19-14

19.3 Corn/Soybean Rotations (Poor N Management Scenario)........................................ 19-15

19.3 Corn/Soybean Rotations (Poor N Management Scenario)........................................ 19-16

19.3.1 Nitrogen Fertilizer Use ........................................................................................ 19-17

19.3.2 Summary of Avoided GHG Emissions................................................................ 19-19

19.3.3 Summary............................................................................................................. 19-20

19.3.4 Sensitivity Summary............................................................................................ 19-22

19.4 References.................................................................................................................. 19-25

20 CONTINUOUS CORN IN THE U.S. CORN BELT .............................................................. 20-1
xlviii

20.1 Introduction.................................................................................................................... 20-1

20.2 Continuous Corn (Base Cases).................................................................................... 20-1

20.2.1 Increases in Soil Organic Carbon......................................................................... 20-3

20.2.2 Costs...................................................................................................................... 20-5

20.2.3 GHG Effects in Addition to Carbon Storage in Soil Organic Matter..................... 20-5

20.2.3.1 Fuel Use........................................................................................................ 20-5

20.2.3.2 Machinery Manufacture, Transportation, and Repair................................... 20-6

20.2.3.3 N Fertilizer Use.............................................................................................. 20-7

20.2.3.4 Herbicide Use................................................................................................ 20-8

20.2.3.5 N2O Emissions from Soil............................................................................... 20-8

20.2.3.6 Summary of Avoided GHG Emissions........................................................ 20-10

20.2.4 Summary............................................................................................................. 20-10

20.2.5 Sensitivity Summary............................................................................................ 20-12

20.3 Continuous Corn (Poor N Management Scenario) .................................................... 20-13

20.3 Continuous Corn (Poor N Management Scenario) .................................................... 20-14

20.3.1 N Fertilizer Use.................................................................................................... 20-15

20.3.2 Summary of Avoided GHG Emissions................................................................ 20-17

20.3.3 Summary............................................................................................................. 20-18

20.3.4 Sensitivity Summary............................................................................................ 20-20

20.4 References.................................................................................................................. 20-23

21 WHEAT/FALLOW IN THE WESTERN GREAT PLAINS.................................................... 21-1

21.1 Introduction.................................................................................................................... 21-1

21.2 Wheat/Fallow (Base Cases) ......................................................................................... 21-1

21.2.1 Increases in Soil Organic Carbon......................................................................... 21-1

21.2.2 Costs...................................................................................................................... 21-3

21.2.3 GHG Effects in Addition to Carbon Storage in Soil Organic Matter..................... 21-4

21.2.3.1 Fuel Use........................................................................................................ 21-4

21.2.3.2 Machinery Manufacture, Transportation, and Repair................................... 21-4

21.2.3.3 N Fertilizer Use.............................................................................................. 21-5

21.2.3.4 Herbicide Use................................................................................................ 21-5

21.2.3.5 N2O Emissions from Soil............................................................................... 21-5

21.2.3.6 Summary of Avoided GHG Emissions.......................................................... 21-6

21.2.4 Summary............................................................................................................... 21-7

21.2.5 Sensitivity Summary.............................................................................................. 21-9
xlix

21.3 Wheat/Fallow (Sensitivity Cases with No Tillage Effect on N2O Emissions)............. 21-10

21.3 Wheat/Fallow (Sensitivity Cases with No Tillage Effect on N2O Emissions)............. 21-11

21.3.1 Summary of Avoided GHG Emissions................................................................ 21-11

21.3.2 Summary............................................................................................................. 21-12

21.3.3 Sensitivity Summary............................................................................................ 21-12

21.4 References.................................................................................................................. 21-15

22 COTTON IN THE SOUTHEASTERN UNITED STATES.................................................... 22-1

22.1 Introduction.................................................................................................................... 22-1

22.2 Cotton (Base Cases)..................................................................................................... 22-1

22.2.1 Increases in Soil Organic Carbon......................................................................... 22-1

22.2.2 Costs...................................................................................................................... 22-3

22.2.3 GHG Effects in Addition to Carbon Storage in Soil Organic Matter..................... 22-4

22.2.3.1 Fuel Use........................................................................................................ 22-4

22.2.3.2 Machinery Manufacture, Transportation, and Repair................................... 22-5

22.2.3.3 N Fertilizer Use.............................................................................................. 22-5

22.2.3.4 Herbicide Use................................................................................................ 22-5

22.2.3.5 N2O Emissions from Soil............................................................................... 22-5

22.2.3.6 Summary of Avoided GHG Emissions.......................................................... 22-6

22.2.4 Summary............................................................................................................... 22-7

22.2.5 Sensitivity Summary.............................................................................................. 22-9

22.3 Cotton (Sensitivity Cases with No Tillage Effect on N2O Emissions)........................ 22-10

22.3 Cotton (Sensitivity Cases with No Tillage Effect on N2O Emissions)........................ 22-11

22.3.1 Summary of Avoided GHG Emissions................................................................ 22-11

22.3.2 Summary............................................................................................................. 22-12

22.3.3 Sensitivity Summary............................................................................................ 22-12

22.4 References.................................................................................................................. 22-15

23 SUMMARY COMPARISONS AND CONCLUSIONS.......................................................... 23-1

23.1 Transport and Storage .................................................................................................. 23-1

23.2 Capture, Transport and Storage................................................................................... 23-2

23.3 Forest management...................................................................................................... 23-4

23.4 Cropland via Reducing Tillage...................................................................................... 23-5

23.5 Direct Verses Indirect Sequestration............................................................................ 23-7

23.6 Conclusions................................................................................................................. 23-10
l

23.7 REFERENCES............................................................................................................ 23-10

A DETAILED LIFE-CYCLE GHG EMISSIONS AVOIDED VIA CAPTURING AND 

STORING CO2.............................................................................................................................A-1

B EXPLANATION OF PROCESS INPUT AND SUMMARY SPREADSHEETS.......................B-1

Process Input sheet ................................................................................................................B-1

Design Basis Section .........................................................................................................B-1

Economic Section...............................................................................................................B-1

EOR and ECBMR Storage Options Only......................................................................B-1

All Storage Options........................................................................................................B-2

Summary Sheet.......................................................................................................................B-2

Summary Sheet Section 1 - Levelized Annual CO2 Storage Costs (years 1-20) .............B-2

Summary Sheet Section 2 - Levelized Annual CO2 Capture and Net Storage Costs 

(years 1-20) ........................................................................................................................B-2

Summary Sheet Section 3 - CO2 Capture and Storage Costs, NPV Basis (years 1-

100).....................................................................................................................................B-3

Summary Sheet Section 4 - Cost of Electricity, $/MWh....................................................B-3

C STATUS AND COSTS OF EXISTING FORESTRY SEQUESTRATION PROJECTS..........C-1

References..............................................................................................................................C-4
li

D PUBLISHED COSTS OF FORESTRY CARBON SEQUESTRATION ..................................D-1

References..............................................................................................................................D-2

E ESTIMATING ECONOMIC INCENTIVES REQUIRED TO ACHIEVE ADOPTION OF 

CONSERVATION-TILLAGE SYSTEMS IN SELECTED REGIONS OF THE UNITED 

STATES.......................................................................................................................................E-1

Background .............................................................................................................................E-1

Budgeting Case Study Tillage Systems .................................................................................E-2

Estimating the Incentives to Adopt Reduced-Tillage Practices..............................................E-2

Analytical Procedure ...............................................................................................................E-3

Discussion of Incentive Level Results ....................................................................................E-7

Sensitivity of Incentives to Changes in Revenue ...................................................................E-8

References Cited...................................................................................................................E-11

Attachment E-1 of Appendix E: Budget and Yield Sources .................................................E-13

Attachment E-2 of Appendix E: Site Selection ....................................................................E-16

Attachment E-3 of Appendix E: Yield Comparisons............................................................E-17

F ESTIMATING STATE-LEVEL SOC RESPONSES.................................................................F-1

References..............................................................................................................................F-9

References............................................................................................................................F-10

liii

LIST OF FIGURES 

Figure 1-1 Preliminary Block Diagram for Aquifer Storage Concept..........................................1-3

Figure 1-2 Preliminary Block Diagram for EOR Concept...........................................................1-5

Figure 1-3 Preliminary Block Diagram for Depleted Oil and Gas Reservoir Storage.................1-7

Figure 1-4 Preliminary Block Diagram for Coal Bed Storage Concept ......................................1-9

Figure 1-5 Preliminary Block Diagram for Ocean Storage Concept.........................................1-11

Figure 1-6 Preliminary Block Diagram for Forest Storage Concept.........................................1-13

Figure 1-7 Preliminary Block Diagram for Cropland Sink Concept ..........................................1-15

Figure 2-1 Carrying charge factor vs. after-tax discount rate (based on constant dollar 

analysis) .............................................................................................................................2-4

Figure 2-2 Relationships among CO2 produced, captured, and avoided when comparing 

power plants with and without CO2 capture (based on Table 2-2) .....................................2-6

Figure 2-3 Relationships among GHG produced, CO2 captured, and GHG avoided on a 

LC basis for CO2 capture and storage (via EOR) combined; based on Tables 2-2 

and 2-3 .............................................................................................................................2-10

Figure 3-1 Carbon dioxide pressure enthalpy diagram..............................................................3-4

Figure 4-1 Pipeline transport cost model overview diagram ......................................................4-5

Figure 4-2 Diameter for the base case as a function of CO2 mass flow rate .............................4-6

Figure 4-3 Breakdown of pipeline cost on a dollar per mile basis..............................................4-7

Figure 4-4 Breakdown of pipeline cost on a percentage of total cost basis...............................4-8

Figure 4-5 Regression analysis of pipeline construction cost data ............................................4-9

Figure 4-6 Total cost per tonne of CO2 for the base case as a function of CO2 mass flow 

rate ...................................................................................................................................4-10

Figure 4-7 Comparison of pipe diameter values ......................................................................4-13

Figure 4-8 Comparison of pipeline capital cost values ............................................................4-14

Figure 5-1 EOR block flow diagram ...........................................................................................5-6

Figure 5-2 EOR cost model overview diagram ..........................................................................5-8

Figure 5-3 Sensitivity analysis for EOR ...................................................................................5-21

Figure 6-1 ECBMR block flow diagram......................................................................................6-5

Figure 6-2 ECBMR cost model overview diagram ....................................................................6-7

Figure 6-3 Well drilling cost as a function of depth ....................................................................6-9

Figure 6-4 Sensitivity analysis for ECBMR ..............................................................................6-19

Figure 7-1 Depleted oil, gas, and aquifer block flow diagram ....................................................7-5

Figure 7-2 Geologic storage cost model overview diagram.......................................................7-6
liv 

Figure 7-3 CO2 injectivity as a function of CO2 mobility .............................................................7-8

Figure 7-4 Well drilling cost as a function of depth ..................................................................7-10

Figure 7-5 Sensitivity analysis for depleted gas reservoir........................................................7-22

Figure 7-6 Sensitivity analysis for depleted oil reservoir..........................................................7-24

Figure 7-7 Sensitivity analysis for aquifer ................................................................................7-25

Figure 7-8 Comparison of CO2 injectivity values......................................................................7-28

Figure 7-9 Comparison of onshore well drilling cost values.....................................................7-28

Figure 7-10 Comparison of offshore well drilling cost values...................................................7-29

Figure 8-1 Cumulative distribution of population and power plant CO2 emissions ....................8-2

Figure 8-2 Ocean pipeline cost model overview diagram ..........................................................8-3

Figure 8-3 Diameter for the base case as a function of CO2 mass flow rate .............................8-4

Figure 8-4 Comparison of pipe diameter values ......................................................................8-11

Figure 8-5 Comparison of subsea pipeline capital cost values................................................8-12

Figure 9-1 Ocean tanker cost model overview diagram ............................................................9-3

Figure 9-2 Sensitivity analysis for ocean tanker ......................................................................9-10

Figure 10-1 Quantities of carbon sequestered (medium productivity sites).............................10-7

Figure 10-2 Carbon sequestered in Spanish Cedar plantations at different site 

productivities ....................................................................................................................10-8

Figure 10-3 Carbon sequestered in vegetation, soil etc. and timber products (Spanish 

Cedar) ..............................................................................................................................10-9

Figure 17-1 Emissions from land use change in Chiapas, Mexico, over 20 years from 

the mid-1970’s..................................................................................................................17-4

Figure 17-2 Map of two key causal factors in land-use change: Distance to roads and 

tracks and population density...........................................................................................17-5

Figure 17-3 Regional baseline for 2000 to 2020, showing expected emissions tc/ha .............17-6

Figure 18-1 Mechanisms by which conservation-tillage systems affect the amount of 

carbon that remains stored in soil organic matter ............................................................18-3

Figure 18-2 Increases in soil organic carbon (SOC) via switching from intensive-till to notill......................................................................................................................................18-4

Figure 18-3 Trends in soil organic carbon (SOC) in the central corn belt................................18-5

Figure 18-4 Counties with more than 20,000 hectares of cropland .........................................18-6

Figure 18-5 Simulated changes in soil organic carbon (SOC) as affected by tillage and 

cropping systems in eastern Colorado.............................................................................18-7

Figure 18-6 Simulated changes in soil organic carbon (SOC) as affected by tillage and 

cropping systems in central Indiana.................................................................................18-8

Figure 19-1 State SOC response curves for switching from intensive-till to no-till (NT) or 

moderate-till (MT) in Iowa and Indiana.............................................................................19-3

Figure 20-1 State SOC response curves for switching from intensive-till to no-till (NT) or 

moderate-till (MT) in Iowa and Indiana.............................................................................20-3

Figure 23-1 CO2 transport and storage cost comparison on a levelized annual $/t of CO2

equivalent life cycle greenhouse gas avoided basis ........................................................23-2
lv

Figure 23-2 CO2 capture, transport and storage cost comparison on a NPV $/t of CO2

equivalent life cycle greenhouse gas avoided basis ........................................................23-3

Figure 23-3 Forestry cost comparison on a NPV $/t of CO2 equivalent life cycle 

greenhouse gas avoided basis ........................................................................................23-5

Figure 23-4 Cropland cases NPV $/t CO2 equivalent life cycle greenhouse gas avoided 

basis.................................................................................................................................23-6

Figure 23-5 Comparison of direct and indirect sequestration cases........................................23-7

Figure 23-6 Comparison of direct and indirect sequestration cases including high and 

low cases. ........................................................................................................................23-8

Figure F-1 Quadratic regression and transformation procedure for SOC response to 

reducing tillage ...................................................................................................................F-2

Figure F-2 Linear relationship between E and ∆SOCs for the Indiana data from 

Table F-1............................................................................................................................F-3

Figure F-3 Linear relationship between E and ∆SOCs for the Iowa data from Table F-2..........F-3

lvii

LIST OF TABLES

Table 1-1 Concepts Status .......................................................................................................... 1-2

Table 1-2 Rationale for Including Aquifers in the Economic Study............................................. 1-4

Table 1-3 Rationale for Including EOR in the Economic Study.................................................. 1-6

Table 1-4 Rationale for Including Depleted Oil and Gas Reservoirs in the Economic 

Study .................................................................................................................................... 1-8

Table 1-5 Rationale for Including Coal Bed Storage in the Economic Study........................... 1-10

Table 1-6 Rationale for Including Ocean Storage in the Economic Study ............................... 1-12

Table 1-7 Rationale for Including Forest Sinks in the Economic Study.................................... 1-14

Table 1-8 Rationale for Including Cropland Sinks in the Economic Study ............................... 1-16

Table 2-1 Financial parameters for calculating the levelized carrying charge factor ................. 2-2

Table 2-2 Calculation of CO2 capture costs on CO2 captured and CO2 avoided bases*........... 2-5

Table 2-3 Summary of Enhanced Oil Recovery on a LC GHG avoided basis .......................... 2-8

Table 2-4 Greenhouse gas emissions from CO2 storage operations ...................................... 2-11

Table 3-1 Summary of parameters for IGCC power plant with CO2 removal............................. 3-3

Table 3-2 Typical composition of recovered coal mine gas........................................................ 3-5

Table 3-3 Greenhouse gas life cycle analysis............................................................................. 3-8

Table 4-1 CO2 pipelines in the United States.............................................................................. 4-2

Table 4-2 Design bases for pipeline transport .......................................................................... 4-10

Table 4-3 Capital and O&M cost inputs for the pipeline transport base and sensitivity 

cases .................................................................................................................................. 4-11

Table 4-4 Overland pipelines’ characteristics ........................................................................... 4-12

Table 5-1 Six largest CO2-EOR projects 1.................................................................................... 5-2

Table 5-2 Estimated CO2 effectiveness for selected CO2-EOR projects5,6................................ 5-9

Table 5-3 Average enhanced oil production per day per well for six largest CO2-EOR 

projectsi.............................................................................................................................. 5-10

Table 5-4 Ratio of producers to injectors for selected CO2-EOR projects 5,6............................ 5-11

Table 5-5 Capital and O&M cost estimation factors.................................................................. 5-13

Table 5-6 Design basis for the EOR base case....................................................................... 5-15

Table 5-7 Recycle compressor’s parameters and cost............................................................. 5-16

Table 5-8 Lease equipment...................................................................................................... 5-17

Table 5-9 Capital and O&M cost inputs for the EOR base case............................................. 5-18

Table 5-10 Process input sheet for base case EOR................................................................. 5-19
lviii

Table 5-11 Summary sheet for base case EOR ...................................................................... 5-20

Table 5-12 EOR base, high cost and low cost cases ............................................................... 5-21

Table 5-13 Results for EOR base, high cost and low cost cases............................................. 5-22

Table 5-14 EOR cost estimation functions15............................................................................. 5-22

Table 5-15 Comparison of EOR cost results15,16....................................................................... 5-23

Table 6-1 Capital and O&M cost estimation factors.................................................................. 6-10

Table 6-2 Design basis for ECBMR base case......................................................................... 6-12

Table 6-3 ECBMR gathering compressor design basis............................................................ 6-12

Table 6-4 ECBMR sales gas compressor design basis............................................................ 6-13

Table 6-5 Lease equipment....................................................................................................... 6-14

Table 6-6 Capital and O&M cost inputs for the ECBMR base case......................................... 6-15

Table 6-7 Process input sheet for base case ECBMR.............................................................. 6-17

Table 6-8 Summary sheet for base case ECBMR .................................................................... 6-18

Table 6-9 ECBMR base, high cost and low cost cases ............................................................ 6-20

Table 6-10 Results for ECBMR base, high cost and low cost cases........................................ 6-20

Table 6-11 CO2-ECBMR cost estimates from Wong, et al.5..................................................... 6-21

Table 6-12 Comparison of CO2-ECBMR cost results5,14........................................................... 6-21

Table 7-1 Capital and O&M cost estimation factors/functions.................................................... 7-9

Table 7-2 Natural gas and oil reservoir property data............................................................... 7-10

Table 7-3 Design bases for natural gas reservoir storage option............................................. 7-11

Table 7-4 Design bases for oil reservoir storage option ........................................................... 7-11

Table 7-5 Aquifer property data................................................................................................. 7-11

Table 7-6 Incomplete sets of aquifer property data ................................................................. 7-13

Table 7-7 Design bases for aquifer storage option ................................................................... 7-14

Table 7-8 Capital and O&M cost inputs for gas and oil reservoir, and aquifer base cases ..... 7-15

Table 7-9 Process input sheet for base case depleted gas reservoir...................................... 7-16

Table 7-10 Summary sheet for base case depleted gas reservoir.......................................... 7-17

Table 7-11 Process input sheet for depleted oil reservoir base case....................................... 7-18

Table 7-12 Summary sheet for depleted oil reservoir base case............................................. 7-19

Table 7-13 Process input sheet for aquifer storage base......................................................... 7-20

Table 7-14 Summary sheet for the aquifer storage base case................................................. 7-21

Table 7-15 Depleted gas reservoir base, high cost and low cost cases .................................. 7-22

Table 7-16 Results for depleted gas reservoir base, high cost and low cost cases................. 7-23

Table 7-17 Depleted oil reservoir base, high cost and low cost cases..................................... 7-24

Table 7-18 Results for depleted oil reservoir base, high cost and low cost cases................... 7-25

Table 7-19 Aquifer base, high cost and low cost cases............................................................ 7-26

Table 7-20 Results for aquifer base, high cost and low cost cases.......................................... 7-26

Table 7-21 Injection schemes’ characteristics .......................................................................... 7-27

Table 8-1 Capital and O&M cost estimation factors.................................................................... 8-5
lix

Table 8-2 Design basis for ocean storage via pipeline ............................................................... 8-5

Table 8-3 Capital and O&M cost inputs for the ocean pipeline base case................................. 8-6

Table 8-4 Process input sheet for the ocean pipeline base case............................................... 8-7

Table 8-5 Summary sheet for the ocean pipeline base case...................................................... 8-8

Table 8-6 Ocean pipeline base, high cost and low cost cases................................................... 8-9

Table 8-7 Results for ocean pipeline base, high cost and low cost cases............................... 8-10

Table 8-8 Subsea pipelines’ characteristics.............................................................................. 8-10

Table 9-1 Capital and O&M cost estimation factors.................................................................... 9-5

Table 9-2 Design basis for ocean storage via tanker.................................................................. 9-6

Table 9-3 Capital and O&M cost inputs for the ocean tanker base case................................... 9-7

Table 9-4 Process input sheet for the ocean tanker base case................................................. 9-8

Table 9-5 Summary sheet for the ocean tanker base case........................................................ 9-9

Table 9-6 Ocean tanker base, high cost and low cost cases ................................................... 9-11

Table 9-7 Results for ocean tanker base, high cost and low cost cases.................................. 9-11

Table 10-1 Summary of forestry sequestration cases .............................................................. 10-6

Table 10-2 Global N2O sources............................................................................................... 10-10

Table 10-3 Forest and baseline N2O And CH4 emissions....................................................... 10-11

Table 11-1 Productivity classes for Loblolly Pine in the southern United States ..................... 11-2

Table 11-2 Expected carbon storage from Loblolly Pine plantations over 100 years.............. 11-3

Table 11-3 Forestry input variables for Loblolly Pine............................................................... 11-5

Table 11-4 Forestry summary of results for Loblolly Pine medium productivity case............. 11-6

Table 11-5 Forestry summary of results for Loblolly Pine high productivity case................... 11-7

Table 12-1 Productivity classes for Douglas Fir in pacific northwest USA............................... 12-2

Table 12-2 Expected carbon storage from Douglas Fir plantations over 100 years................ 12-3

Table 12-3 Forestry input variables for Douglas Fir.................................................................. 12-5

Table 12-4 Forestry summary of results for Douglas Fir medium productivity case................ 12-6

Table 12-5 Forestry summary of results for Douglas Fir high productivity case..................... 12-7

Table 13-1 Productivity classes for Spanish Cedar in central America.................................... 13-3

Table 13-2 Components of baseline carbon stock (tC)............................................................. 13-4

Table 13-3 Expected carbon storage from Spanish Cedar over 100 years ............................. 13-4

Table 13-4 Forestry input variables for Spanish Cedar ............................................................ 13-6

Table 13-5 Forestry summary of results for Spanish Cedar medium productivity case.......... 13-7

Table 13-6 Forestry summary of results for Spanish Cedar low productivity case................. 13-8

Table 13-7 Forestry summary of results for Spanish Cedar high productivity case................ 13-9

Table 14-1 Productivity classes for Pinus Oocarpa in Chiapas, Mexico.................................. 14-3

Table 14-2 Components of baseline carbon stock (tC)............................................................. 14-4

Table 14-3 Expected carbon storage from Pine-Oak forest enriched with Pinus Oocarpa 

over 100 years ................................................................................................................... 14-4

Table 14-4 Forestry input variables for Pine-Oak ..................................................................... 14-6
lx

Table 14-5 Forestry summary of results for Pine-Oak medium productivity case................... 14-7

Table 14-6 Forestry summary of results for Pine-Oak low productivity case.......................... 14-8

Table 14-7 Forestry summary of results for Pine-Oak high productivity case......................... 14-9

Table 15-1 Expected carbon storage from restoration of Miombo woodland over 100 

years................................................................................................................................... 15-3

Table 15-2 Forestry input variables for Miombo....................................................................... 15-5

Table 15-3 Forestry summary of results for Miombo medium productivity case..................... 15-6

Table 16-1 Productivity factors for mixed orchards of Tamarind and Mango........................... 16-2

Table 16-2 Expected carbon storage from Mango-Tamarind agro-forestry over 100 y.......... 16-4

Table 16-3 Forestry input variables for Mango-Tamarind........................................................ 16-6

Table 16-4 Forestry summary of results for Mango-Tamarind medium productivity case...... 16-7

Table 17-1 Percentage of above-ground carbon lost from 1976 to 1996................................. 17-5

Table 17-2 Predicted annual rate of deforestation (%). ............................................................ 17-7

Table 17-3 Forestry input values for avoidance of deforestation.............................................. 17-9

Table 17-4 Forestry summary of results for Avoidance of Deforestation medium 

productivity case.............................................................................................................. 17-10

Table 18-1 Adoption of conservation-tillage practices in the eastern corn belt and midsouth U.S. .......................................................................................................................... 18-9

Table 18-2 Adoption of conservation-tillage practices in the western corn belt .................... 18-10

Table 18-3 Adoption of conservation-tillage practices in major small grain states in 

year 2000 ......................................................................................................................... 18-11

Table 18-4 Adoption of conservation-p in major cotton states in year 2000 .......................... 18-12

Table 19-1 Effects of switching from intensive-till to no- or moderate-till on SOC, GHG 

Emissions, and costs of life-cycle (LC) avoided GHG emissions--Indiana and Iowa 

state averages for Corn/Soybean rotation......................................................................... 19-2

Table 19-2 Example process and economic input variables for switching from intensivetill to no-till for corn/soybean rotation in Indiana................................................................ 19-4

Table 19-3 Reductions in fuel use and associated carbon emissions by switching from 

intensive-till to no-till in a corn/soybean rotation ............................................................... 19-6

Table 19-4 Reductions in fuel use and associated carbon emissions by switching from 

intensive-till to moderate-till in a corn/soybean rotation.................................................... 19-7

Table 19-5 Mean N rates for corn in the ten major U.S. corn producing states--199513.......... 19-9

Table 19-6 Example spreadsheet output for estimating herbicide carbon equivalent 

emissions for corn/soybean rotation................................................................................ 19-10

Table 19-7 Example spreadsheet output for carbon equivalent emissions from changes 

in N2O emissions for corn/soybean rotation when changing from intensive-till to notill ...................................................................................................................................... 19-11

Table 19-8 Example spreadsheet output summarizing changes in GHG emissions for 

corn/soybean rotation when changing from intensive-till to no-till .................................. 19-12

Table 19-9 Example spreadsheet output summarizing costs and life-cycle (LC) avoided 

GHG on a carbon equivalent (CE) basis for corn/soybean rotation when changing 

from intensive-till to no-till ................................................................................................ 19-13
lxi

Table 19-10 Corn/soybean rotation sensitivity summary....................................................... 19-15

Table 19-11 Effects of switching from intensive-till using 150 kg N/ha to no- till using 175 

or 200 kg N/ha on GHG emissions and costs of life-cycle (LC) avoided GHG 

emissions—Indiana Corn/Soybean rotations .................................................................. 19-16

Table 19-12 Corn/soybean carbon emissions associated with important combinations of 

N source and placement.................................................................................................. 19-17

Table 19-13 Corn/soybean overall increase in N2O emissions due to both switching from 

intensive-till to no-till and increasing N fertilizer use....................................................... 19-19

Table 19-14 Corn/soybean example spreadsheet output summarizing changes in GHG 

emissions due to switching from intensive-till to no-till and increasing N fertilizer use.. 19-20

Table 19-15 Corn/soybean example spreadsheet output summarizing costs and lifecycle (LC) avoided GHG on a carbon equivalent (CE) basis when changing from 

intensive-till to no-till and increasing N fertilizer use....................................................... 19-21

Table 19-16 Corn/soybean example spreadsheet sensitivity summary output when 

changing from intensive-till to no-till and increasing N fertilizer use............................... 19-23

Table 19-17 Effects of increases in N rate and N2O emissions on amounts and costs of 

avoided GHG via switching from intensive-till to no-till in a Corn/Soybean rotation....... 19-24

Table 20-1 Effects of switching from intensive-till to no- or moderate-till on SOC, GHG 

emissions, and costs of life-cycle (LC) avoided GHG emissions--Indiana and Iowa 

state averages for Continuous Corn.................................................................................. 20-2

Table 20-2 Example process and economic input variables for switching from intensivetill to no-till for continuous corn in Indiana ......................................................................... 20-4

Table 20-3 Reductions in fuel use and associated carbon emissions by switching from 

intensive-till to no-till in continuous corn............................................................................ 20-6

Table 20-4 Reductions in fuel use and associated carbon emissions by switching from 

intensive-till to moderate-till in continuous corn ................................................................ 20-7

Table 20-5 Example spreadsheet output for estimating herbicide carbon equivalent 

emissions for continuous corn........................................................................................... 20-8

Table 20-6 Example spreadsheet output for carbon equivalent emissions from changes 

in N2O emissions for continuous corn when changing from intensive-till to no-till ........... 20-9

Table 20-7 Example spreadsheet output summarizing changes in GHG emissions for 

continuous corn when changing from intensive-till to no-till............................................ 20-10

Table 20-8 Costs and life-cycle (LC) avoided GHG on a carbon equivalent (CE) basis 

for continuous corn when changing from intensive-till to no-till ...................................... 20-11

Table 20-9 Continuous corn sensitivity summary.................................................................. 20-13

Table 20-10 Effects of switching from intensive-till Using 150 kg N/ha to no- till Using 

175 or 200 kg N/ha on GHG emissions and costs of life-cycle (LC) avoided GHG 

emissions—Indiana Continuous Corn............................................................................. 20-14

Table 20-11 Continuous corn carbon emissions associated with important combinations 

of N source and placement.............................................................................................. 20-15

Table 20-12 Continuous corn overall increase in N2O emissions due to both switching 

from intensive-till to no-till and increasing N fertilizer use............................................... 20-17
lxii

Table 20-13 Continuous sorn example spreadsheet output summarizing changes in 

GHG emissions due to switching from intensive-till to no-till and increasing N 

fertilizer use...................................................................................................................... 20-18

Table 20-14 Continuous corn costs and life-cycle (LC) avoided GHG on a carbon 

equivalent (CE) basis when changing from intensive-till to no-till and increasing N 

fertilizer use...................................................................................................................... 20-19

Table 20-15 Continuous corn example spreadsheet sensitivity summary output when 

changing from intensive-till to no-till and increasing N fertilizer use............................... 20-21

Table 20-16 Effects of increases in N rate and N2O emissions on amounts and costs of 

avoided GHG via switching from intensive-till to no-Continuous Corn........................... 20-22

Table 21-1 Effects of switching from intensive-till to no-till on SOC, GHG emissions, and 

costs of life-cycle (LC) avoided GHG emissions—Wheat/Fallow in the western great 

plains .................................................................................................................................. 21-2

Table 21-2 Example process and economic input variables for switching from intensivetill to no-till for wheat/fallow rotation................................................................................... 21-3

Table 21-3 Reductions in fuel use and associated carbon emissions by switching from 

intensive-till to no-till in a wheat/fallow rotation ................................................................. 21-4

Table 21-4 Example spreadsheet output for estimating herbicide carbon equivalent 

emissions for wheat/fallow rotation.................................................................................... 21-5

Table 21-5 Example spreadsheet output for carbon equivalent emissions from changes 

in N2O emissions for wheat/fallow rotation when changing from intensive-till to no-till.... 21-6

Table 21-6 Example spreadsheet output summarizing changes in GHG emissions for 

wheat/fallow rotation when changing from intensive-till to no-till ...................................... 21-7

Table 21-7 Example spreadsheet output summarizing costs and life-cycle (LC) avoided 

GHG on a carbon equivalent (CE) basis for wheat/fallow rotation when changing 

from intensive-till to no-till .................................................................................................. 21-8

Table 21-8 Wheat/fallow rotation sensitivity summary........................................................... 21-10

Table 21-9 Wheat/fallow example spreadsheet output summarizing changes in GHG 

emissions due to switching from intensive-till to no-till with no change in N2O 

emissions ......................................................................................................................... 21-11

Table 21-10 Wheat/fallow costs and life-cycle (LC) avoided GHG on a carbon equivalent 

(CE) basis when changing from intensive-till to no-till with no change in N2O 

emissions ......................................................................................................................... 21-13

Table 21-11 Wheat/fallow example spreadsheet sensitivity summary output when 

changing from intensive-till to no-till with no change in N2O emissions.......................... 21-14

Table 22-1 Effects of switching from intensive-till to no-till on SOC, GHG emissions, and 

costs of life-cycle (LC) avoided GHG emissions—Cotton in the southeastern United 

States ................................................................................................................................. 22-2

Table 22-2 Example process and economic input variables for switching from intensivetill to no-till for cotton.......................................................................................................... 22-3

Table 22-3 Reductions in fuel use and associated carbon emissions by switching from 

intensive-till to no-till for cotton .......................................................................................... 22-4

Table 22-4 Example spreadsheet output for estimating herbicide carbon equivalent 

emissions for cotton........................................................................................................... 22-5
lxiii

Table 22-5 Example spreadsheet output for carbon equivalent emissions from changes 

in N2O emissions for cotton when changing from intensive-till to no-till ........................... 22-6

Table 22-6 Example spreadsheet output summarizing changes in GHG emissions for 

cotton when changing from intensive-till to no-till.............................................................. 22-7

Table 22-7 Example spreadsheet output summarizing costs and life-cycle (LC) avoided 

GHG on a carbon equivalent (CE) basis for cotton when changing from intensive-till 

to no-till............................................................................................................................... 22-8

Table 22-8 Cotton rotation sensitivity summary...................................................................... 22-10

Table 22-9 Cotton example spreadsheet output summarizing changes in GHG emissions 

due to switching from intensive-till to no-till with no change in N2O emissions .............. 22-11

Table 22-10 Cotton costs and life-cycle (LC) avoided GHG on a carbon equivalent (CE) 

basis when changing from intensive-till to no-till with no change in N2O emissions ...... 22-13

Table 22-11 Cotton example spreadsheet sensitivity summary output when changing 

from intensive-till to no-till with no change in N2O emissions.......................................... 22-14

Table 23-1 CO2 transport and storage cost comparison on a levelized annual CO2

equivalent life cycle greenhouse gas avoided basis ......................................................... 23-1

Table 23-2 CO2 capture, transport and storage cost comparison on a NPV equivalent life 

cycle greenhouse gas avoided basis ................................................................................ 23-3

Table 23-3 Forestry cases NPV cost comparison (LC equivalent greenhouse gas 

avoided, including above and below ground carbon and timber products & 

revenues). .......................................................................................................................... 23-4

Table 23-4 Cropland cases NPV equivalent life cycle greenhouse gas avoided basis ........... 23-6

Table 23-5 Base-case forest and cropland areas required to offset LC GHG emissions 

avoided by base-case CO2 capture and storage options.................................................. 23-9

Table A-1 EOR base case detailed analysis of LC GHG emissions avoided.............................A-2

Table A-2 ECBMR base case detailed analysis of LC GHG emissions avoided .......................A-3

Table A-3 Aquifer storage base case detailed analysis of LC GHG emissions avoided...........A-4

Table A-4 Depleted oil reservoir base case detailed analysis of LC GHG emissions 

avoided.................................................................................................................................A-5

Table A-5 Depleted gas reservoir base case detailed analysis of LC GHG emissions 

avoided.................................................................................................................................A-6

Table A-6 Ocean pipeline storage base case detailed analysis of LC GHG emissions 

avoided.................................................................................................................................A-7

Table A-7 Ocean tanker storage base case detailed analysis of LC GHG emissions 

avoided.................................................................................................................................A-8

Table C-1 Forestry sequestration projects initiated until mid-1998 ............................................C-2

Table D-1 Published costs of forestry carbon sequestration......................................................D-1

Table E-1 Yield mean, standard deviation, and coefficient of variation and variable costs 

at selected sites...................................................................................................................E-5

Table E-2 Incentive levels under base yields and prices............................................................E-6

Table E-3 Base yields and revenues by experimental site and rotation.....................................E-9

Table E-4 Base prices................................................................................................................E-10

Table E-5 Revenue sensitivity equations ..................................................................................E-10
lxiv

Table F-1 Increases in SOC due to switching from intensive-till to no-till or moderate till 

in Indiana..............................................................................................................................F-4

Table F-2 Increases in SOC due to switching from intensive-till to no-till or moderate-till 

in Iowa..................................................................................................................................F-7

1-1

1

INTRODUCTION

In order to plan for potential CO2 mitigation mandates, energy companies need better cost 

information on CO2 mitigation options, especially storage and sink enhancement options that 

involve non-energy company operations. One of the major difficulties in evaluating CO2 storage 

and sink enhancement options is obtaining consistent, transparent, accurate, and comparable 

economics. This project compares the economics of major technologies and practices under 

development for CO2 storage and sink enhancement, including options for storing captured CO2, 

such as active oil reservoirs, depleted oil and gas reservoirs, deep aquifers, coal beds, and oceans, 

as well as the enhancement of biological sinks such as forests and croplands.

The primary funding source for the project was the Department of Energy (DOE), with the 

Tennessee Valley Authority (TVA) and the Electric Power Research Institute (EPRI) also 

providing additional funds. An international group of experts in this area was assembled to 

develop the technology/practice designs, the economic premises, and cost evaluations. TVA was 

the prime for this project, responsible for overall completion of the effort. EPRI organized 

efforts to select specific sequestration processes to be evaluated for captured CO2 and 

coordinated the efforts of consultants from the MIT, SFA Pacific, and the IEA Greenhouse Gas 

Programme and the Edinburgh Centre for Carbon Management (ECCM) to develop and refine 

the framework for the economic evaluations. MIT and Parsons Infrastructure and Technology 

developed process designs for captured CO2 storage processes and helped TVA develop 

economic models for comparing technologies and practices. The University of Tennessee 

Agricultural Policy Research Center, in collaboration with TVA, evaluated the economics of 

enhancing CO2 sequestration in croplands. The IEA Greenhouse Gas Programme developed the 

concept design for evaluating the forest sequestration options.

1.1 Economic Framework

Most of the cost comparisons to date have concentrated on CO2 capture, with the assumption that 

CO2 sequestration is a small part of these costs. In addition, these comparisons have used 

information supplied from studies of specific technologies, and the variability in costs due to 

variability in assumptions and lack of visibility into assumptions lessens the usefulness of the 

results. In the case of sequestration, virtually no comparative economic evaluations of processes 

have been done.

Methodologies for developing economic comparisons are generally available. They range from 

very detailed ±10 percent for site-specific evaluations, where final decisions are made between 

options, to very general economics with little insight into the economic premises that were used 

to develop the economics. The latter is usually a simplification of more detailed economics for 
Introduction

1-2

very high-level comparisons. In some cases, probability analyses are included to help evaluate 

risks. This usually adds significantly to the complexity of the model and the time to develop 

results. The model may use simplified economics to allow probability analysis without making 

the model too complex to run in a reasonable time.

The economic evaluations developed for this project are between the ranges described above and 

are typical of prior EPRI economics where a ±25 to 30 percent estimate is made, with the ability 

to modify values to be relatively site-specific. Material balances are made, equipment lists and 

pricing are developed, installation costs are estimated, and contingencies are estimated for 

project and process uncertainties. These types of evaluations are intended to be transparent, 

consistent, and comparable. They are consistent with the EPRI economics of advanced power 

generation with CO2 capture being developed for DOE. Probability analysis was not included to 

keep the results consistent with other EPRI studies.

The economic framework also included life-cycle analysis for the various sequestration options. 

This means that all greenhouse gas emissions from cradle to grave were estimated and 

considered in the analysis. The economic analysis used spreadsheet models that are flexible 

enough to allow a wide variation in the range of parameters to be evaluated and the sensitivity 

cases to be run. The CO2 storage options were sized to accommodate the CO2 from our basecase capture plant (see the design basis in Chapter 3). In addition, the amount of forest and 

cropland required to sequester the same amount of CO2 over a 100-year planning horizon was 

calculated.

1.2 Concepts Evaluated

The potential processes and concepts to be evaluated were initially prioritized, and the concepts 

were placed into three categories—(1) included, (2) may be included but more information is 

needed before a final decision can be made, and (3) not included due to the lack of good 

information at this time. Because one of the most unique aspects of this work is the comparison 

between storage of captured CO2 and sink enhancement, it was felt that at least one of each type 

should be included. The list of concepts and their final status is presented in Table 1-1.

Table 1-1

Concepts Status

Included Not Included

Oil Reservoirs Mineralization

Depleted Oil and Gas 

Reservoirs

Ocean Storage

Forests

Croplands

Coal Beds
Introduction

1-3

In the case of ocean fertilization and mineralization, it is felt that at this time, there is not enough 

reasonable information to develop a meaningful concept description and that these processes 

should not be included until more R&D is performed.

1.2.1 Aquifer Storage

Shown below is the preliminary block diagram for the Aquifer Storage concept. This concept is 

simple in application. The complexity comes from deciding on the nature of the distribution and 

number of wells.

Distribution

CO2 Injection

Pipeline CO2

(7,389 tpd)

Figure 1-1

Preliminary Block Diagram for Aquifer Storage Concept

The rationale for including the aquifer concept in the economic evaluation is summarized in 

Table 1-2 below. This concept has the largest storage capacity of all the concepts, except the 

ocean, and is widespread throughout the United States. In addition, it is at commercial scale, 

although not in the United States. Sufficient data is believed to be available.
Introduction

1-4

Table 1-2

Rationale for Including Aquifers in the Economic Study

Merits

Potential 

Challenges Applicability

Technical 

Maturity Data Availability

Industrial 

Acceptance

Compatibility 

With Power 

Systems

• Best potential 

CO2 storage 

capacity of all 

geological 

storage options

• Retention time 

predicted to be 

thousands of 

years

• Offshore 

aquifers 

eliminate most 

safety concerns

• Understanding 

risk of 

catastrophic or 

slow release of 

CO2

• Ubiquitous and 

large, so 

widespread 

availability

• Some 

experience of 

aquifer storage 

for chemicals, 

etc.

• Little actual 

experience for 

this specific 

application

• Many studies on 

this storage 

option

• Specific 

reservoir 

characterization 

is lacking

• Commercial 

application—

CO2 has been 

injected into the 

Utsira formation 

under the North 

Sea since 

August 1996, as 

part of the 

Sleipner Vest 

project

• Accepted for 

materials other 

than CO2

• Excellent
Introduction

1-5

1.2.2 Oil Reservoir Storage with Enhanced Oil Recovery (EOR)

Shown below is the preliminary block diagram for the EOR storage concept.

Distribution Oil Treaters

(existing)

CO2 Compression

Recycle

Producing Well

Operation

Water

Treatment

CO2 Injection

Sales

Oil

Pipeline CO2

(7,389 tpd)

CO2 / Oil

Separation

Figure 1-2

Preliminary Block Diagram for EOR Concept

The rationale for including the EOR concept in the economic evaluation is summarized in 

Table 1-3 below. While this concept has a more limited storage capacity and is not as 

widespread, it is likely to be an early application due to the potential for low-cost storage. It is 

also commercial in the United States. However, it has not been optimized for maximum CO2

storage, and its compatibility with power systems is of some concern. Sufficient data is believed 

to be available.
Introduction

1-6

Table 1-3

Rationale for Including EOR in the Economic Study

Merits

Potential 

Challenges Applicability

Technical 

Maturity Data Availability

Industrial 

Acceptance

Compatibility 

With Power 

Systems

• Oil by-product 

makes option 

economically 

attractive

• Not considered 

to involve any 

undue risks to 

man or the 

natural 

environment

• Injection of CO2

done 

commercially 

today

• Could often be 

cheaper to 

obtain CO2 from 

natural sources

• Global storage 

capacity may be 

limited (e.g., to 

65 Gt C) (2)

• For today’s 

blowdown, 

reservoir 

operations need 

to store CO2

under pressure

• Limited to areas 

where there are 

active oil fields 

• EOR practiced 

on a significant 

scale for last 

25 years

• Excellent • EOR is widely 

used, in 1998 

more than 65 oil 

fields in the U.S. 

were being 

injected with 

CO2

• Industry actively 

investigating the 

option of using 

captured CO2

• Oil operations 

require 

continuous 

supply (versus 

intermittent)

• Issues with 

fluctuation in the 

quantity of CO2

needed over 

time
Introduction

1-7

1.2.3 Depleted Oil and Gas Reservoir Storage

Shown in Figure 1-3 is the preliminary block diagram for the Depleted Oil and Gas Reservoir 

Storage concept.

Distribution

CO2 Injection

Pipeline CO2

(7,389 tpd)

Figure 1-3

Preliminary Block Diagram for Depleted Oil and Gas Reservoir Storage

The rationale for including the Depleted Oil and Gas Reservoir Storage concept in the economic 

evaluation is summarized in Table 1-4 below. This concept is similar to the EOR, except the 

storage location is simply used for storage without recovery of oil or gas. Since the storage 

location has a known integrity, it should be relatively straightforward to use. The gas reservoirs 

may be the easiest since gas should be depleted and the reservoir can just be repressurized. The 

data is believed to be sufficient, since it is so similar to EOR.
Introduction

1-8

Table 1-4

Rationale for Including Depleted Oil and Gas Reservoirs in the Economic Study

Merits

Potential 

Challenges Applicability

Technical 

Maturity Data Availability

Industrial 

Acceptance

Compatibility 

With Power 

Systems

• Global storage 

capacity as 

much as 140 Gt 

C for disused 

gas fields and 

40 Gt C for 

disused oil fields 

(3)

• Reservoirs have 

proven 

containment 

over geological 

timeframes

• Knowledge 

about reservoir 

already exists

• Today very few 

reservoirs 

depleted

• Understanding 

risk of 

catastrophic or 

slow release of 

CO2

• Limited to areas 

where there are 

disused oil and 

gas reservoirs

• Uses similar 

technology to 

EOR

• Good • No commercial 

scheme 

involving such 

fields as yet 

exists

• May be liability 

issues

• May need 

multiple 

reservoirs for 

large power 

plants
Introduction

1-9

1.2.4 Coal Bed Storage

Shown below is a preliminary block diagram for the Coal Bed Storage concept.

Distribution

CO2 Injection

Pipeline CO2

(7,389 tpd)

Figure 1-4

Preliminary Block Diagram for Coal Bed Storage Concept

The rationale for including the Coal Bed Storage concept in the economic evaluation is 

summarized in Table 1-5 below. While the data is limited, CH4 by-product production credits 

and significant coal deposits make a good argument for inclusion. Data availability is limited.
Introduction

1-10

Table 1-5

Rationale for Including Coal Bed Storage in the Economic Study

Merits

Potential 

Challenges Applicability

Technical 

Maturity Data Availability

Industrial 

Acceptance

Compatibility 

With Power 

Systems

• CH4 by-product 

makes option 

economically 

attractive

• CO2 strongly 

sequestered by 

adsorption on 

coal matrix

• Worldwide large 

coal deposits 

means 

potentially large 

CO2 storage 

capacity

• Enhanced gas 

recovery (EGR) 

methods for coal 

bed CH4

exploitation 

require further 

refinement

• Unclear as to 

how many types 

of coal 

formations will 

be practical to 

use for coal bed 

CH4 production

• Injection of CO2

into coal beds 

already used to 

enhance CH4

recovery, 

although

process is still at 

an early stage of 

development

• Limited • Well accepted • Could be used 

to develop a 

zero greenhouse 

gas emissions 

power plant 

fueled by 

coalbed CH4, 

where waste 

CO2 produced 

by plant is 

injected into 

coalbed CH4

reservoirs to 

produce more 

CH4
Introduction

1-11

1.2.5 Ocean Storage

Shown in Figure 1-5 is a preliminary block diagram for the Ocean Storage concept.

Transportation to

Injection Site

CO2 Injection

Pipeline CO 2

(22,167 tpd)

Power

Plant 1

Power

Plant 3

Power

Plant 2

(7,389 tpd) (7,389 tpd) (7,389 tpd)

Figure 1-5

Preliminary Block Diagram for Ocean Storage Concept

The rationale for including the Ocean Storage concept in the economic evaluation is summarized 

in Table 1-6 below. The ocean has the largest storage capacity of any of the concepts, and much 

work has been done to study ways to store CO2 in the ocean. Sufficient data should be available.
Introduction

1-12

Table 1-6

Rationale for Including Ocean Storage in the Economic Study

Merits

Potential

Challenges Applicability

Technical 

Maturity Data Availability

Industrial 

Acceptance

Compatibility 

With Power 

Systems

• Largest potential 

sink for CO2, 

storage capacity 

estimated to be 

upwards of 

1000 Gt C (4)

• Leaks do not 

pose safety 

issues

• Could have a 

negative impact 

on local marine 

environment

• Significant legal 

and jurisdictional 

issues to be 

overcome

• Negatively 

perceived by 

nongovernmental 

organizations 

(NGOs)

• Retention time, 

on the order of 

hundreds of 

years, less than 

for underground 

storage

• Best suited to 

countries 

situated 

adjacent to 

ocean trenches 

and that do not 

have access to

suitable 

underground 

reservoirs, for 

example, Japan

• Populated areas 

are near 

coastlines

• Much 

experience from 

offshore 

exploration/ 

production is 

applicable

• Modest • Not well 

perceived 

compared to 

geological 

storage options

• Field experiment 

to take place off

the coast of 

Hawaii in 2001, 

this should help 

to reduce some 

of the 

uncertainties

• Excellent for 

plants situated 

on coastline
Introduction

1-13

1.2.6 Forest Sink Enhancement

Shown in Figure 1-6 is a preliminary block diagram for the Forest Sink Enhancement concept.

CO2

Forest

Growth

Forest

7,389 tpd CO2 net

Forest Fires,

pests, etc.

Carbon

in trees and soil

CO2

Figure 1-6

Preliminary Block Diagram for Forest Storage Concept

The rationale for including the Forest Sink Enhancement concept in the economic evaluation is 

summarized in Table 1-7 below. Forests are generally considered the lowest-cost storage option, 

and a great deal of work has been done on them. This is the basic sink comparison to be made 

with the captured storage concept. A number of concerns still remain, and matching the 

economics will be difficult. Sufficient data should be available.
Introduction

1-14

Table 1-7

Rationale for Including Forest Sinks in the Economic Study

Merits

Potential 

Challenges Applicability

Technical 

Maturity Data Availability

Industrial 

Acceptance

Compatibility 

with power 

systems

• Low cost

• Significant forest 

available

• Provides funding 

and employment 

in rural areas 

and developing 

countries

• Preservation of 

biodiversity

• Monitoring and 

verification of 

carbon storage

• Opportunities for 

fraud

• “Leakage” 

minimization

• Short-term 

storage

• Risks of forest 

loss through 

fires, pests, and 

social factors

• Particularly 

applicable to 

areas of low 

population with 

few other land 

use options

• Changes to 

albedo may 

make forests 

less effective in 

high latitudes

• Global capacity 

limited and costs 

increase 

substantially as 

less favorable 

sites are used

• Forestry is 

technically 

mature

• Land owners 

and farmers 

need to be 

educated on 

merits of forestry 

for carbon 

storage

• Monitoring and 

verification 

services offered 

but further 

developments 

would be 

beneficial to 

increase 

accuracy and 

reduce costs

• Good

• Current projects 

small and may 

not be 

representative of 

large schemes

• Current largescale projects 

are mainly 

deforestation 

avoidance

• Still being 

debated at the 

COP 6 meeting

• Considered the 

easy, low-cost 

option

• Some 

companies 

already buying 

forestry carbon 

credits

• Still concerns 

over “leakage” 

and risks

• Applicable to all 

power systems 

since there is no 

direct link to the 

power plant
Introduction

1-15

1.2.7 Cropland Sink Enhancement

Shown in Figure 1-7 is a preliminary block diagram for the Cropland Sink Enhancement 

concept. The cropland concept involves enhancing soil carbon sequestration by switching from 

conventional- to conservation-tillage systems and improving residue management. 

Conservation-tillage systems use less intensive tillage, often no tillage, and leave at least 

30 percent of the crop residues on the soil surface. Conservation-tillage systems also sometimes 

include a winter cover crop that remains on the soil surface to reduce soil erosion. The winter 

cover crop is not harvested and adds additional crop residue to soil organic matter.

General parameters for estimating the net cost of switching to conservation-tillage systems are 

presented in the block diagram below. The net cost of switching to conservation-tillage systems 

is the change in cost of tillage-system inputs, plus or minus the change in revenue and risk from 

changes in crop yield.

General parameters for estimating the additional CO2 sequestered in soil organic matter are also 

presented in the block diagram. These parameters are (1) the increase in crop residue carbon 

added to soil organic matter, (2) the reduced rate of soil organic matter decomposition to CO2, 

and (3) the reduced soil erosion and the associated reduction of CO2 emitted from eroded soil.

Conservation-

 Tillage System

& Residue

Management

Crop Yield

Crop

Residue

Carbon

Production

Soil Organic

Matter

(7,389 tpd

CO2Net)

Eroded

Soil CO2

CO2CO2

Figure 1-7

Preliminary Block Diagram for Cropland Sink Concept

The rationale for including the Cropland Sink Enhancement concept in the economic evaluation 

is summarized in Table 1-8 below. The cropland component of this project will estimate added 

costs of converting from conventional-tillage systems to conservation-tillage systems that 

sequester additional carbon in soil organic matter. Increased adoption of conservation-tillage 

systems and improved residue management accounts for about one-half of the potential for 

reducing greenhouse (GHG) emissions from U.S. croplands. The remaining one-half of the 

potential for reducing GHG emissions from U.S. croplands is highly fragmented and beyond the 

scope and resources of this project.
Introduction

1-16

Table 1-8

Rationale for Including Cropland Sinks in the Economic Study

Merits

Potential 

Challenges Applicability

Technical 

Maturity Data Availability

Industrial 

Acceptance

Compatibility 

with power 

systems

• Relatively low 

projected 

cost/ton of CO2

• Collateral 

benefits of 

conservation 

tillage—

improved soil 

quality, reduced 

soil erosion, 

improved wateruse efficiency, 

improved crop 

productivity 

where well 

adapted

• Possible need 

for periodic use 

of conventional 

tillage to 

maintain crop 

productivity, 

resulting in 

partial loss of 

sequestered 

CO2

• Possible 

reversion to 

conventional 

tillage due to 

changes in land 

ownership

• Resistance to 

including 

biological sinks 

in GHG polices

• Poorly 

developed 

infrastructure for 

CO2 credits and 

markets

• Good base for 

infrastructure

• Excellent in welldrained soils, 

water deficient 

cropping 

systems, and 

highly erosive 

soils

• Moderately good 

in most other 

cropping 

systems

• Conservation 

tillage systems 

under 

development 

since early 

1970s

• ~35% adoption 

achieved to date 

in U.S.

• Technology 

ready for rapid 

adoption, given 

additional 

economic 

incentives

• Good for costs 

of tillage 

systems

• Moderately good 

for CO2

sequestration 

rates

• Lacking for 

equilibrium 

levels of 

sequestered 

carbon and time 

to equilibrium

• Good for CO2

emissions 

factors 

associated with 

tillage-system 

inputs

• Generally good 

farmer 

acceptance 

because of 

collateral 

benefits

• Somewhat 

greater 

economic risk to 

farmers

• May require 

moderate 

adoption 

incentives to 

achieve rapid 

additional 

adoption

• Good 

compatibility via 

combining farmlevel CO2

sequestration 

credits into 

bundles of 

sufficient size to 

match power 

project needs
Introduction

1-17

In conventional-tillage systems, soil is plowed or otherwise thoroughly tilled, and all of the crop 

residues are mixed with soil. In contrast, conservation-tillage systems involve less intensive 

tillage (often no tillage), leave 30 percent or more of the crop residues on the soil surface, and 

sequester additional carbon in soil organic matter that otherwise would be emitted to the 

atmosphere as CO2. In addition to sequestering more carbon, conservation-tillage systems also 

have lower emissions associated with production and use of tillage-system inputs, dramatically 

reduce soil erosion and CO2 emissions from eroded soil, improve soil quality, and conserve soil 

water by reducing water runoff and evaporation from the soil.

1.3 Report Organization

Presented in Chapter 2 is a summary of the methodology used in developing the costs. Chapters 

3-9 contain the information on the captured CO2 storage concepts. Chapters 10-17 contain the 

information on the forestry options. Chapters 18-22 contain the information on the croplands 

options. Chapter 23 contains the summary comparisons between options and conclusions.

1.4 References

1 DOE/EPRI. 2000. Evaluation of innovative fossil-fuel power plants with CO2 removal. 

EPRI., Palo Alto, California; U.S. Department of Energy–Office of Fossil Energy, 

Germantown, Maryland, and U.S. Department of Energy/NETL, Pittsburgh, Pennsylvania: 

1000316.

2-1

2

METHODOLOGIES FOR CALCULATING THE COST OF 

CAPTURING AND STORING CO2

General procedures for calculating the cost of capturing and storing CO2 are overviewed in this 

chapter. They include:

• Calculating costs using revenue requirement methodology

• Defining the three cost bases used in this report: CO2 captured, CO2 avoided, and CO2

equivalent life-cycle (LC) greenhouse gas (GHG) avoided

• Combining CO2 capture and storage costs

• Accounting for differences in timing of CO2 sequestration profiles and for leaking 

reservoirs

Specific procedures are provided in Chapter 3.

2.1 Revenue Requirement Methodology

Costs of storing captured CO2 were calculated using the same general approach as for CO2

capture costs that have already been calculated by EPRI and DOE for several power plant CO2

capture technologies1. This allowed CO2 capture and storage costs to be combined on an equal 

basis.

2.1.1 Approach for CO2 Capture

In the DOE/EPRI project, EPRI revenue requirement (RR) methodology was used2. Revenue 

requirement methodology is used for regulated economics (i.e., return on equity and debt are set 

at regulated levels) and focuses on minimizing annual revenue requirements, whereas 

unregulated economics focus on maximizing return on investment. With regulated economics, 

utilities are allowed to charge a price for electricity that recovers revenue requirements for 

prudent investments. A levelized RR ($/yr) was calculated for each year of the 20-year plant 

book life as follows:

Levelized RR = Levelized Carrying Charge (LCC) + Expenses

 = Levelized annual cost of electricity (1)
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-2

where LCC = Total Plant Cost (or TPC) x Levelized Carrying Charge Factor (or LCCF), and 

Expenses include O&M and fuel costs. The TPC includes process facilities capital, general 

facilities capital, engineering and home office overhead, project and process contingencies, and 

miscellaneous expenses generally included under owners costs.

A levelized revenue requirement was calculated for an integrated gasification combined cycle 

(IGCC) plant without CO2 capture (Case 3b of reference 1) and for the same IGCC plant 

converted to capture CO2 (Case 3a of reference 1) as shown in Equation 1. A value of 0.15 for 

the LCCF was based on assumptions specified in the DOE/EPRI report for return on debt, return 

on equity, federal and state income taxes, book depreciation, property taxes, and insurance. The 

same financial parameters and associated LCCF of 0.15 were used for calculating the base case 

for cost of storing captured CO2. Assumptions for return on debt, return on equity, and federal 

and state income taxes are presented in Table 2-1.

The cost of electricity (COE) for the capture and non-capture power plants was calculated based 

on their respective LCC and expenses. The difference in COE ($/MWh) for capture and noncapture IGCC plants was used as a basis for calculating the cost of CO2 captured (more details 

provided in Section 2.2).

Table 2-1

Financial parameters for calculating the levelized carrying charge factor

Current 

dollars Constant dollars

Percent 

of total Cost, % Return, % Cost, % Return, %

Debt 45 9.0 4.05 5.83 2.62

Preferred stock 10 8.5 0.85 5.34 0.53

Common stock 45 12.0 5.40 8.74 3.93

Total annual return 100 10.30 7.09

Inflation rate, % 3.00

Federal tax, % 34.00

State tax, % 4.15

Federal and state tax, % 38.00

Discount rates

After tax 8.76 6.09

Before tax 10.3 7.09
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-3

2.1.2 Adjustment for CO2 Storage

In calculating the costs of storing captured CO2, the RR methodology for CO2 capture was 

generalized to accommodate options for enhanced revenues from CO2 storage. These 

adjustments were required because CO2 storage options such as enhanced oil recovery (EOR) 

and enhanced coal bed methane recovery (ECBMR) generate revenues that at least partially 

offset the cost of CO2 storage. This required that the RR methodology for CO2 capture be 

generalized as follows:

Levelized RR = LCC + O&M costs - Enhanced revenues

 = Levelized annual net cost of storing CO2 (2)

A positive net storage cost equals the breakeven CO2 tipping fee for EOR. A negative net 

storage cost equals the breakeven CO2 purchase price for EOR.

2.1.3 Adjustment of Levelized Carrying Charge Factor as a Function of Discount 

Rate

Costs of capturing and storing CO2 were calculated on a constant 1999 dollar basis. Financial 

parameters in Table 2-1 result in a LCC factor of 0.150 and an after-tax discount rate (r) of 

6.09 percent on a constant-dollar basis. Discounted costs and revenues were used to compare 

CO2 storage and sink enhancement options differing in timing of costs and in timing and 

permanence of CO2 storage and sink enhancement (see Section 2.4). The discount rate used in 

Section 2.4 calculations needs to be consistent with the LCC factor. The discount rate and LCC 

factor are related by Equation (3).

LCC factor = 0.0738 + 0.01 r + 0.0004 r2(3)

Equation (3) is based on the regression in Figure 2-1. Levelized carrying charge factors were 

calculated for the range of after-tax discount rates (r) in Figure 2-1 by varying return to debt and 

equity and holding other financial parameters constant. Procedures from reference 2 were used 

for these calculations.
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-4

y = 0.0004x2 + 0.01x + 0.0738

R2 = 1

0.000

0.050

0.100

0.150

0.200

0.250

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00

After-tax discount rate, %

Levelized carrying charge factor

Figure 2-1

Carrying charge factor vs. after-tax discount rate

(based on constant dollar analysis)

2.1.4 Impact of CO2 Storage Costs on Cost of Electricity

The COE for the IGCC CO2 capture plant was adjusted for the net cost of CO2 storage 

(i.e., storage costs minus enhanced oil or gas revenues). The increase in COE due to CO2 storage 

was calculated by dividing the total revenue requirement for CO2 storage by the total MWh of 

electricity produced. Consistent with revenue requirement methodology, CO2 storage operations 

were assumed to be owned by a regulated utility or reimbursed by a regulated utility for net 

storage costs (including return on equity), in which case enhanced oil or gas revenues were 

assumed to have no impact on income taxes. The reason is that regulated utilities are allowed to 

charge a price for electricity that recovers revenue requirements for prudent investments. Within 

this context, enhanced oil or gas revenues reduce the revenue requirement for CO2 storage, and 

thereby, reduce any increase in electricity prices that a regulated utility would be allowed to 

charge to cover CO2 storage costs. The end result is no additional income tax burden beyond 

that built into the carrying charge. In some EOR and ECBMR scenarios, enhanced oil or gas 

revenues are large enough to result in a negative revenue requirement for storing CO2.

In these cases, it was assumed that the negative revenue requirement would be used to:

• Eliminate the increase in electricity prices that a regulated utility would normally be allowed 

to charge to cover CO2 storage costs, and
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-5

• Reduce part of the increase in electricity prices that a regulated utility would normally be 

allowed to charge to cover CO2 capture costs.

2.2 Capture Costs on CO2 Captured, CO2 Avoided, and Life-Cycle GHG

Avoided Bases

2.2.1 CO2 Captured Basis

The cost per tonne of CO2 captured was calculated by subtracting the cost of electricity (COE, 

$/MWh) for the IGCC reference plant without CO2 capture from the COE for the IGCC plant 

with CO2 capture and dividing by the amount of CO2 captured (tonne/MWh). Assuming no 

losses of captured CO2 during transport and storage, this calculation also provides the capture 

cost per tonne of CO2 stored. Inputs for calculating CO2 capture costs on a CO2 captured basis 

are presented in Table 2-2. For this case, the CO2 capture cost on a CO2 captured basis = (55.08-

43.98)/0.763 = $14.55/tonne CO2 captured.

Table 2-2

Calculation of CO2 capture costs on CO2 captured and CO2 avoided bases*

Based on IGCC example from DOE/EPRI report1

Capture plant Reference plant

COE, $/MWh 55.08 43.98

CO2 produced, tonne/MWh 0.836 0.718

CO2 captured, tonne/MWh 0.763 0.000

Direct CO2 emitted, tonne/MWh 0.073 0.718

LC GHG emitted, tonne CO2 eq./MWh

(see Table 3) 0.111 0.750

Cost, CO2 captured basis: Costs

CO2 capture cost (CO2 captured basis), $/tonne CO2 captured 14.55

Costs, CO2 avoided basis:

CO2 capture cost (CO2 avoided basis), $/tonne CO2 avoided 17.21

CO2 capture cost (LC GHG avoided basis), $/tonne CO2 eq. avoided 17.37

*

Assumes 80% capacity factor.
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-6

2.2.2 CO2 Avoided Basis

The primary difference in capturing CO2 for commercial markets versus capturing CO2 for 

sequestration is the role of energy. In the former case, energy is a commodity, and all we care 

about is its price. In the latter case, using energy generates more CO2 emissions, which is 

precisely what we want to avoid. We can account for this “energy penalty” by calculating costs 

on a CO2 avoided basis. As shown in Figure 2-2, due to the extra energy required to capture 

CO2, the amount of CO2 emissions avoided is always less than the amount of CO2 captured. 

Therefore, capturing CO2 for purposes of sequestration requires more emphasis on reducing 

energy inputs than in traditional commercial processes.

CO2 Avoided during CO2 Capture

0 0.2 0.4 0.6 0.8 1 1.2

Capture Plant

Reference

Plant

CO2 Produced, tonne/MWh

CO2 Emitted

CO2 Captured

CO2 Captured

CO2 Avoided

Figure 2-2

Relationships among CO2 produced, captured, and avoided when comparing power plants 

with and without CO2 capture (based on Table 2-2)

The cost per tonne of CO2 avoided in the CO2 capture process was calculated with Equation(4) 

from the EPRI/DOE report1:

( ) ( )

( ) ( )

(4)

/ /

$ / $ /

2 2

2

tonneCO emitted MWh tonne CO emitted MWh

MWh MWh

Cost of CO Avoided

withoutcapture withcapture

withcapture withoutcapture

−

−

=

Inputs for calculating CO2 capture costs on a CO2 avoided basis are presented in Table 2-2. For

this case, the cost of CO2 avoided = (55.08 – 43.98)/(0.718-0.073) = $17.21/tonne CO2 avoided.
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-7

2.2.3 LC GHG Avoided Basis

The calculation of CO2 capture costs on a life-cycle greenhouse gas (LC GHG) avoided basis is 

the same as in the previous section exc ept that the terms in the denominator of Equation (4) are 

tonne CO2 equivalent LC GHG emitted/MWh instead of tonne CO2 directly emitted from the 

power plant/MWh. Life-cycle GHG emissions are greater than direct CO2 emissions for two 

reasons. First, greenhouse gases in addition to CO2 were included in the analysis. Second, the 

system boundaries were expanded to include coal mining and manufacture and transportation, 

construction, and decommissioning of power plant equipment in addition to GHG emissions 

from power plant operations; this boundary expansion was required because of the energy 

penalty and associated added coal required for CO2 capture and because of the energy use

associated with added equipment required for CO2 capture. Inputs for calculating CO2 capture 

costs on a LC GHG avoided basis are presented in Table 2-3. For this case, the cost of GHG 

avoided on a LC GHG basis = (55.05-43.98)/(0.111-0.750) = $17.37/tonne CO2 equivalent LC 

GHG avoided. Calculation of LC GHG emissions (values in the denominator of the equation in 

the previous sentence) is illustrated in Table 2-3. The 100-year warming potentials used are 

from the recent Intergovernmental Panel on Climate Change (IPCC) report7.

2.3 Combining Capture and Storage Costs

The previous section described the bases based only on capture costs and emissions. In this 

section, we describe how these bases are modified to also consider storage costs and emissions. 

In addition, we describe the methodology for reporting storage costs alone on a LC GHG 

avoided basis.

2.3.1 CO2 Captured Basis

Costs associated with CO2 compression, transportation, and storage are included in the cost of 

electricity (COE, $/MWh) from a CO2 capture plant. The cost per tonne of CO2 captured is 

calculated by subtracting the COE for the reference power plant without CO2 capture from the 

COE for the power plant with CO2 capture, including costs for compression, transportation, and 

storage, and dividing the difference by the amount of CO2 captured (tonne/MWh).
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-8

Table 2-3

Summary of Enhanced Oil Recovery on a LC GHG avoided basis

Construction $ of Total Plant TPC

Coal Flow Carbon Carbon Rail Transport Flue Gas Energy Cost per Tonne Power Plant

tonne/day wt. % Emission, % Km tonne/day GJ/tonne of Steel M$

Case 3a 3,474 63.75% 8.68% 100 51,599 28 $50,000 $662,484

Case 3b 3,136 63.75% 99.70% 100 53,383 28 $50,000 $535,983

With CO2 Capture (Case 3a) Without CO2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N20 CH4 CO 2 N 20 CH 4

Coal Mine 33,698 0.5 1.6 Coal Mine 30,417 0.5 1.4

Mine Methane Vented 639 minor 2,901 Mine Methane Vented 577 minor 2,619

Coal Train 1,821 minor minor Coal Train 1,821 minor minor

Power Plant Emissions 205,812 11.0 minor Power Plant Emissions 2,133,828 11.4 minor

Power Plant Construction 1,243 0.0 0.0 Power Plant Construction 1,006 0.0 0.0

Power Plant Decommissioning 124 0.0 0.0 Power Plant Decommissioning 101 0.0 0.0

Total Power Plant 243,338 12 2,903 Total Power Plant 2,167,749 12 2,620

100 Year Multiplier 1 296 23 100 Year Multiplier 1 296 23

Total Power Plant CO2 Equivalent 243,338 3,433 66,762 Total Power Plant CO2 Equivalent 2,167,749 3,530 60,262

Total (mg CO2 Equivalent / kWh) 86,053 1,214 23,609 Total (mg CO 2 Equivalent / kWh) 728,612 1,186 20,255

Power Plant Total ( mg CO 2 Equivalent / kWh) 110,876 Power Plant Total ( mg CO 2 Equivalent / kWh) 750,053

Compression of Captured CO2 to Pipeline Spec

CO2 Compression

Compression Power 2,650 kW 1,598 0.1 19

Construction 6.1 0.0 0.0

Decommissioning 0.6 0.0 0.0

Total 1,605 0.1 19

100 Year Multiplier 1 296 23

CO2 Equivalent 1,605 23 438

CO2 Compression Total (CO 2 Equivalent ) 2,066

Transportation & Sequestration of Captured CO 2

CO2 Transportation

Pipeline Construction 913 0.0 0.0

Pipeline Decommissioning 91 0.0 0.0

Total 1,005 0 0

CO2 Sequestration-EOR

Power 25,142 kW 128,378 1 155

Plant Construction

 Aboveground Equipment 279 0.0 0.0

 Subsurface Equipment 64 0.0 0.0

Plant Decommissioning 34 0.0 0.0

Total 128,755 1 155

CO2 Transportation & Sequestration Total 129,759 1 155

100 Year Multiplier 1 296 23

CO2 Equivalent 129,759 210 3,569

Transportation & Sequestration Total (CO 2 Equivalent ) 133,538

Amount of Emitted & Avoided GHG (CO 2 Equivalents)

With Without 

Capture CO2 Capture CO2 Capture Difference Storage (CO2 Compression, Transportation, and Sequestration)

kg CO2 equivalent emitted/kWh 0.111 0.750 0.639 Compression CO2 equivalent emitted, tonne/yr 2,066

CO2 equivalent avoided, tonne/yr 1,807,446 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 133,538

Total CO2 equivalent emitted, tonne/yr 135,604

Total System

CO2 equivalent avoided by capture, tonne/yr 1,807,446

CO2 equivalent emitted from storage, tonne/yr 135,604

CO2 equivalent LC avoided, tonne/yr 1,671,842

CO2 equivalent LC avoided, % of CO2 captured 77.5%

CO2 equivalent LC avoided, % of CO2 equivalent avoided during capture 92.5%

Enhanced Oil Recovery: Greenhouse Gas Life-Cycle (LC) Analysis

GHG Emissions

tonnes/year

GHG Emissions

tonnes/year

GHG (CO2 Equivalents) Emmited During CO2 Capture & Storage
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-9

2.3.2 CO2 Avoided Basis (CO2 Capture and Storage Combined)

In addition to the inclusion of CO2 compression, transportation, and storage costs as described in 

section 2.3.1 above, this basis also considers the CO2 emitted during CO2 storage. Referencing 

Equation 4, including storage costs on a CO2 avoided cost basis will increase the $/MWhwith capture

and the tonne CO2 emitted/MWhwith capture terms.

2.3.3 GHG Avoided in CO2 Capture and Storage Processes Combined on a 

Life-Cycle (LC) Basis

The concept of GHG emissions avoided on a LC GHG basis for capture and storage combined is 

illustrated in Figure 2-3. The LC GHG emissions from the reference and capture power plants 

were discussed in Section 2.2.3, and emissions levels from the reference and capture power 

plants are presented in Tables 2-2 and 2-3. The red bars in Figure 2-3 are the LC GHG 

emissions in addition to the direct CO2 emissions from the reference and capture power plants. 

The LC GHG emissions avoided via CO2 capture and storage combined are calculated as the LC 

GHG emitted from the reference plant (blue plus red bars) minus the LC GHG emitted from the 

capture plant and storage operation combined (blue plus red plus yellow bars).

To get the cost per tonne of LC GHG avoided via the capture and storage processes combined, 

the combined annual net cost of capturing and storing CO2 was divided by the annual tonnes of 

CO2 equivalent GHG avoided via capture and storage processes combined. For the EOR 

example in Table 2-3, the annual combined net cost of CO2 capture and storage was 

$6.70 million (data not shown). The annual tonnes of CO2 equivalent GHG avoided in capture 

and storage processes combined was 1.67 million (see next paragraph for details). Dividing 

annual costs by annual tonnes gave a net cost of $4.01/tonne CO2 equivalent GHG avoided by 

capture and storage combined.

The difference in LC GHG emissions from the reference and capture plants is 0.750-0.111=0.639 

tonnes CO2 equivalent/MWh (Table 2-3). This difference in LC GHG emissions was multiplied 

by the MWh/year for the capture plant to get the annual tonnes of CO2 equivalent GHG avoided 

via the capture process (1,807,446 tonnes CO2 equivalent LC GHG avoided/yr). The LC GHG 

emissions from EOR CO2 storage were 135,606 tonnes CO2 equivalent LC GHG/yr, resulting in 

1,807,466-135,606 = 1,671,840 tonnes CO2 equivalent LC GHG avoided/yr for the CO2 capture 

and storage processes combined.
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-10 

Life-Cycle (LC) GHG Avoided: CO2 Capture with 

Storage via EOR

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Capture Plant &

CO2 Storage

Reference Plant

GHG Produced, tonne CO2 equivalents/MWh

CO2 Emitted--Power Plants

∆ GHG w/ LC Analysis--

Power Plants

LC GHG Emitted--CO2

Transport & Storage

CO2 Captured

CO2 Captured

LC GHG Avoided

Figure 2-3

Relationships among GHG produced, CO2 captured, and GHG avoided on a LC basis for 

CO2 capture and storage (via EOR) combined; based on Tables 2-2 and 2-3 

In the case of ocean storage, the portion of injected CO2 predicted to leak back out of the ocean 

(3.1 percent in 100 years from an MIT model) was subtracted from the GHG emissions avoided 

during the CO2 capture and storage processes to get a net GHG emissions avoided. For all other 

cases, the storage location leakage was assumed to be zero. The net GHG emissions avoided 

was used to calculate the cost of CO2 capture and storage on a LC GHG avoided basis. 

2.3.4 Costs of CO2 Storage on a Life-Cycle GHG Avoided Basis (Storage Only) 

The cost per tonne of CO2 stored was calculated on a life-cycle GHG avoided basis (storage 

system only) by dividing the total annual revenue requirement for CO2 storage by the difference 

between annual tonnes CO2 stored and LC GHG emitted due to the storage operation (i.e., CO2

compression, transportation, and sequestration). Examples of LC GHG emissions from CO2

storage operations are presented for base cases in Table 2-4. This analysis included LC GHG 

emitted from energy consumption during equipment manufacture, transportation, construction, 

and decommissioning, and during the storage operation. Detailed LC GHG emissions from EOR 

storage (base case) are presented in Figure 2-3. Detailed LC GHG emissions from all the basecase CO2 storage options in Table 2-4 are presented in Appendix A. 
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-11

Table 2-4

Greenhouse gas emissions from CO2 storage operations

CO2 Storage Option

Direct CO2

emitted

Total LC GHG emitted

(includes direct CO2

emissions)

million tonnes CO2 eq./y

Storage of CO2 from one power plant at 80% capacity factor*:

Enhanced oil recovery 0.130 0.136

Enhanced coalbed methane recovery 0.049 0.052

Deep saline aquifers 0.002 0.003

Depleted oil reservoirs 0.002 0.003

Depleted gas reservoirs 0.002 0.003

Storage of CO2 from three power plants at 80% capacity factor**:

Ocean pipeline 0.008 0.013

Ocean tanker 0.061 0.064

*

Assumes 100 km from power plant to storage operation and storage of 2.158 million 

tonnes CO2/y

**Assumes 100 km from power plant to ocean shore and 100 km from shore to ocean 

injection point; and storage of 6.474 million tonnes CO2/y.

2.4 Comparing the Economics of CO2 Storage and Sink Enhancement 

Options Differing in Timing and Permanence of Sequestration

Carbon dioxide storage and sink enhancement options differ greatly in timing of costs, the 

uptake or sequestration of CO2 and, in some cases, leakage of CO2 back into the atmosphere. 

For options involving storage of captured CO2, the primary costs and amounts of CO2 stored are 

levelized over the book life of the CO2 capture and storage plants which is assumed to be 

20 years in this project. With storage of captured CO2, there are also relatively small monitoring 

costs after 20 years, and in some cases, there may be additional transaction costs after 20 years. 

In some cases such as ocean storage, there are leaks of CO2 back to the atmosphere that occur 

after 20 years. The costs and CO2 sink enhancement for forestry projects usually extend over at 

least a 50- to 100-year period. In cropland systems involving reduction of tillage to achieve CO2

sink enhancement, primary costs in the form of annual farmer adoption incentives will likely be 

required for 5 to 20 years after a farmer switches to a reduced-tillage system. Sink enhancement 

occurs over approximately the first 20 to 40 years after switching to reduced tillage practices, 

and changes in GHG emissions associated with crop production inputs such as reduced fuel use 

continue for as long as the reduced-tillage system is used. Monitoring costs, and perhaps 
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-12

additional transaction costs, will continue for as long as the reduced-tillage system is used. If a 

farmer permanently switches back to a more intensive tillage system because of crop production 

problems, the CO2 sink reduces in size, resulting in CO2 loss back to the atmosphere.

The differences in timing and permanence discussed above present a significant challenge in 

comparing the economics of diverse options such as storage of captured CO2 and CO2 sink 

enhancement in forests and cropland. There have been many suggestions on how to handle this 

challenge4, 6. Removals and emissions/leaks are treated as separate events. The idea is that when 

one removes a ton of CO2, one receives the going price of CO2. When a ton of CO2 is released, 

the owner of this CO2 must then purchase a credit from elsewhere at the going price. CO2 prices 

will be set as a result of government policy either through market mechanisms (e.g., a cap and 

trade system) or in the form of a tax (e.g., a carbon tax). As long as CO2 can be emitted in the 

atmosphere for free, there will be no incentive to sequester CO2 on a large-scale.

In order to implement this approach, one must make explicit assumptions concerning CO2 prices 

and discount rates. We argue that no matter what methodology one adopts, they all must make 

assumptions about how to deal with CO2 prices and discount rates. Some methods seem to avoid 

this task by making these assumptions implicitly. We prefer to confront the issue of the longterm value of CO2 storage and sink enhancement and the discount rate directly as these are 

values, that while admittedly highly uncertain, where one can appeal to an underlying rationale.

The mathematical formulation for comparing the value and cost of CO2 storage and sink 

enhancement options differing in timing of costs and in timing and permanence of storage and 

sink enhancement is shown below. This approach provides a breakeven CO2 price ($/tonne of 

CO2 sequestered) for each CO2 storage and sink enhancement option that reflects differences in 

timing of costs and in timing and permanence of CO2 storage and sink enhancement. If the 

calculated breakeven CO2 price is less than the market CO2 price, then the proposed project has 

favorable economics. However, if the calculated breakeven CO2 price is greater than the market 

CO2 price, then the proposed project is not economical. The breakeven carbon price is very 

project specific and care must be taken when trying to generalize the results beyond a given 

project. In this study, we look specifically at sequestration in conjunction with an IGCC power 

plant. Extrapolating the results to other types of power plants is not necessarily valid.

The net present value (NPV) of the revenues from CO2 storage and sink enhancement options 

differing in timing and permanence is calculated as follows3:

∑ ( ) ( )( )

=

=

−

= +

100

0

1

t

t

t

NPV p t a t r (5)

where p is the CO2 price ($/tonne), a is the abatement or avoided emissions (tonnes/yr), r is the 

discount rate, and t is time (yrs). Based on the assessment of cases with constant and variable 

CO2 prices, we concluded that considering variable CO2 prices is beyond the scope of this 

project. Assuming a constant CO2 price (po) results in the following form of Equation (5):
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-13

( )( )

t

t

t

NPV po a t r

−

=

=

= ∑ 1+

100

0

(6)

The relative value of CO2 offsets differing in timing and permanence is compared by setting the 

NPV of CO2 abatement as calculated in Equation (6) equal to the discounted cost (c) of 

achieving the CO2 offset and solving for a constant breakeven price as follows5:

∑ ( )( ) ∑ ( )( )

=

=

− −

=

=

+ = +

100

0

100

0

1 1

t

t

t t

t

t

po a t r c t r (7)

and

( )( )

( )( )

Cost of CO sequestration

a t r

c t r

Breakeven p

t

t

t

t

o 100 2

0

100

0

1

1

=

+

+

=

∑

∑

=

−

=

−

(8)

Equation (8) is used in this project to compare breakeven prices (i.e., costs) for CO2 storage and 

sink enhancement options differing in timing of costs and in timing and permanence of CO2

storage and sink enhancement. Note that computationally a(t) appears to be discounted in 

Equation (8); however, the discount term in the denominator actually arises from discounting 

CO2 revenues as shown in Equation (5).

In this project, Equation (8) is used with a planning horizon of 100. This planning horizon is 

long enough to reflect typical project life cycles for all the CO2 storage and sink enhancement 

options included in this project.

Equation (8) reduces to a ratio of total CO2 abatement costs to total avoided emissions for cases 

in which annual CO2 storage costs and annual avoided emissions are in the same proportion over 

time. For the CO2 capture and storage options in this project, CO2 abatement costs and CO2

avoided emissions are levelized over time for years 1-20. This means that for years 1-20 of the 

CO2 capture and storage options, the breakeven price is the same on a discounted and a nondiscounted basis. However, when monitoring and/or transaction costs are included for 

years 21-100, the breakeven price is affected by discounting. For forestry and cropland options, 

the breakeven price is significantly affected by discounting.

The 6.09 percent after-tax discount rate (constant dollar basis) used for capture and storage of 

CO2 (Table 2-1) was also used in base cases for cropland and forestry CO2 sink enhancement 

options. The rationale for using the same discount rate for the CO2 capture/storage and CO2 sink 

enhancement options is that funding for both types of projects would come from an electric 

utility seeking to receive credits for reducing GHG emissions. A utility likely would use the 

same discount rate for evaluating GHG abatement investments regardless of whether the 

investments involve CO2 capture and storage or CO2 sink enhancement.
Methodologies for Calculating the Cost of Capturing and Storing CO2

2-14

2.5 References

1 DOE/EPRI. 2000. Evaluation of innovative fossil fuel power plants with CO2 removal. 

EPRI, Palo Alto, California; U.S. Department of Energy–Office of Fossil Energy, 

Germantown, Maryland, and U.S. Department of Energy/NETL, Pittsburgh, Pennsylvania: 

1000316.

2 EPRI. 1999. TAG® Technical assessment guide: Volume 3, Revision 8: Fundamentals 

and methods electricity supply. Palo Alto, California. TR-100281-V3R8.

3 Herzog, Howard, Ken Caldeira, and John Reilly. 2002. An issue of permanence: 

Assessing the effectiveness of ocean and carbon sequestration. In review.

4 Marland, G., K. Fruit, and R. Sedjo. 2002. Accounting for sequestered carbon: The 

question of permanence. Environmental Science & Policy. In press.

5 McCarl, Bruce A., Uwe Schnider, Brian Murray, Jimmy Williams, and Ronald D. Sands. 

2001. Economic potential of greenhouse gas emission reductions: Comparative role for soil 

sequestration in agriculture and forestry. First National Conference on Carbon 

Sequestration. http://www.netl.doe.gov/publications/proceedings/01/carbon_seq/4c3.pdf.

6 Noble, I., M. Apps, R. Houghton, D. Lashof, W. Makundi, D. Murdiyarso, B. Murray, 

W. Sombroek, and R. Valentini. 2000. Implications of different definitions and generic 

issues. In: Watson, R. T., I. R. Noble, B. Bolin, N. H. Ravindranath, D. J. Verado, and 

D. J. Dokkens (eds.). Land use, land use change, and forestry. Cambridge University Press, 

Cambridge, United Kingdom, pp. 53-156.

7 Ramaswamy, V., O. Boucher, J. Haigh, D. Hanglustaine, J. Haywood, G. Myhre, 

T. Nakajima, G. Y. Shi, and S. Solomon. “Radiative Forcing of Climate Change.” 2001. 

In: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the 

Third Assessment Report of the Intergovernmental Panel on Climate Change. 

J. T. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell, 

and C. A. Johnson (eds.). Cambridge University Press, Cambridge, United Kingdom and 

New York, New York, USA, 881 pp., 2001.
3-1

3

STORING CAPTURED CO2—BASIS & APPROACH

3.1 Technologies Evaluated

The following transportation and injection processes for captured CO2 were evaluated in this 

study:

• Overland pipeline transport (Chapter 4)

• Enhanced oil recovery (Chapter 5)

• Enhanced coalbed methane recovery (Chapter 6)

• Depleted oil reservoir storage (Chapter 7)

• Depleted gas reservoir storage (Chapter 7)

• Deep saline aquifer storage (Chapter 7)

• Ocean storage via pipeline (Chapter 8)

• Ocean storage via tanker (Chapter 9)

For the processes of ocean fertilization and mineralization, it was determined that there was not 

enough information at this time to develop meaningful conceptual designs and cost estimates. 

Therefore, these were not included in the evaluations pending more R&D.

Initially, the same was thought to be true for coalbed methane. However, a recent IEA 

Greenhouse Gas R&D Programme Report1 assessed the potential of enhanced coalbed methane 

recovery with CO2 sequestration and concluded, “Injection of carbon dioxide into deep coal 

seams has the potential to enhance coal-bed methane recovery, while simultaneously 

sequestering carbon dioxide. Analysis of production operations from the world’s first carbon 

dioxide-enhanced coal-bed methane demonstration plant, in the San Juan Basin, indicates that 

the process is technically and economically feasible. A recent pilot scheme in Alberta, Canada, 

should also help to confirm the technical and economic data of this process.” Thus, while there 

is still uncertainty about the effectiveness of CO2 in enhancing the recovery of coalbed methane, 

the potential is such that it was included in the study.

3.2 Approach

Two key areas for all the geologic storage options are the injection/production wells and field 

equipment/production operations. Two annual surveys, “Joint Association Survey on Drilling 

Costs”2 and “Costs and Indices for Domestic Field Equipment and Production Operations”3, 
Storing Captured Co2—Basis & Approach

3-2

have for many years tracked costs for drilling and operating domestic oil and gas fields. These 

costs are disaggregated by depth, regions, well type, and production rate. Our options were tied 

as closely as possible to these surveys to provide both up-to-date costs and indices that measure 

the increase or decrease in costs from year to year.

A key area for all the options, including the ocean storage options, is the pipeline used to 

transport the captured CO2. The MIT Pipeline Transport Model, developed by the MIT Energy 

Laboratory, 4 was used for pipeline sizing and costs.

For each option, a baseline conceptual design was generated based on the assumptions discussed 

below. From the baseline conceptual design, capital, O&M costs, and an economic analysis with 

several figures of merits were developed in a spreadsheet format. These were then used to 

develop sensitivity analyses and life cycle analyses, again in a spreadsheet format.

3.3 Common Design Basis

A nominal 500 MWe gross integrated gasification combined cycle (IGCC) plant operating at an 

80 percent capacity factor was utilized as the production source of CO2. This was based on the 

DOE/EPRI’s recent study on the “Evaluation of Innovative Fossil Fuel Power Plants with CO2

Removal.”

5

 Table 3-1 shows a summary of the parameters used in this study taken from the 

DOE/EPRI report for Case 3a, “IGCC with CO2 Removal.”
Storing Captured Co2—Basis & Approach

3-3

Table 3-1

Summary of parameters for IGCC power plant with CO2 removal

Parameter Unit Value

Thermal Input, HHV 106 Btu/h 3,723

Gross Power Output MW 490.4

Net Power Output MW 403.5

Efficiency, HHV % 37.0

Capacity Factor % 80

CO2 Captured t/d 7,389

million scm/d 3.76

CO2 Emitted kg/kWh 0.073

CO2 Avoided in Capture t/d 6,246

CO2 Capture Cost $/t captured 14.55

CO2 Capture Cost $/t avoided in 

capture

17.21

Plant Life y 20

Capital Charge Factor % 15.0

Fuel Cost $/GJ 1.18

Fuel Real Esc. Rate %/y 0.00

Fuel Levelization Factor 1.00

TPC $/kW 1,642

Fixed O&M $/kWy 32.98

Variable O&M $/MWh 3.90

Heat Rate, HHV kJ/kWh 9,727

Capital $/MWh 35.04

O&M $/MWh 8.61

Fuel $/MWh 11.44

Levelized cost of 

Energy (LCOE)

$/MWh 55.08

106Btu = 1.06 GJ

The baseline IGCC plant produces two streams of CO2 from the double-stage Selexol acid gas 

removal process. One stream is at 3.4 bar (50 psia), while the second stream produced at 1.0 bar 

(15 psia) is boosted to 3.4 bar (50 psia). The combined 3.4 bar (50 psia) CO2 streams are further 
Storing Captured Co2—Basis & Approach

3-4

compressed and dehydrated in a multi-stage, intercooled compressor to 83 bar (1,200 psia). The 

amount of CO2 recovered from the IGCC plant that must be sequestered is 7,389 tonnes per day.

The existing pipeline specification (152 bar, -40°C dewpoint, N2<300 ppmv, O2<40 ppmv, 

Ar<10 ppmv) was used for the quality of the CO2. The DOE/EPRI study is consistent with this 

except for the CO2 pressure. The DOE/EPRI study was based on compressing the CO2 to 83 bar, 

although a sensitivity study at 152 bar was done. Our study includes additional compression to 

152 bar.

Referring to Figure 3-1, the CO2 at 83 bar (1200 psia) and 41°C (105°F) is above and to the 

liquid side of the critical point 31.1°C (88°F) and 73.0 bar (1073 psi). By increasing pressure to 

152 bar (2200 psi) and 38°C (100°F) or less, the pipeline pressure can drop to about 103 bar 

(1500 psi) before recompression, and the CO2 mass is ensured of retaining flow properties 

approximating a liquid. This boost compression adds an additional power requirement of 

2,650 kWe. The CO2 stream is dried to a -40°C dewpoint and contains N2<300 ppmv, 

O2<40 ppmv, and Ar<10 ppmv to prevent corrosion.

Note: °C=(°F-32)/1.8, 1bar=14.5 psi

Figure 3-1

Carbon dioxide pressure enthalpy diagram

3.4 Greenhouse Gas Emissions Inventory

The atmospheric greenhouse gas emissions for the full fuel cycle including coal mining, 

transportation, power conversion, and carbon sequestration for the EOR option are listed in 

Table 3-3. The emissions are presented in terms of tonnes per year of the gases emitted. These 

figures are based on the heat and material balance data for Case 3a presented in the DOE/EPRI 

recent study on the “Evaluation of Innovative Fossil Fuel Power Plants with CO2 Removal.”

5
Storing Captured Co2—Basis & Approach

3-5

Case 3a was based on an IGCC power system with an output of 404 MWe and a HHV efficiency 

of 37.0 percent. Over 90 percent of the carbon in the coal was captured and sequestered as 

7,389 tonnes per day of CO2 (2.16 million tonnes per year). Annual emissions were based on a 

capacity factor of 80 percent.

Many of the assumptions for emissions from mining, coal transport, and construction, which are 

discussed below, were taken from previous studies, especially the ETSU study. 6 The emissions 

from the power plant were based on DOE/EPRI Case 3a material balance and emissions 

estimate.5

3.4.1 Emissions from Deep Mining

Based on DOE/EPRI Case 3a, the total coal feed to the IGCC plant is 1.01 million tonnes per 

year. The deep-mined coal source in the U.S. is expected to have high methane content. The 

methane content of coal mines can vary widely over short distances. In addition to the methane 

contained in the coal seams, methane also seeps from the strata surrounding the coal mine. The 

amount of pure CH4 released is expected to be about 10 Nm3/tonnes of coal mined.7 This gas is 

partly recovered, mixed with air and other gases to give a total apparent gas release of about 

twice this flow rate. Table 3-2 gives the typical composition of gas recovered from a coal mine.

Table 3-2

Typical composition of recovered coal mine gas

Gas % Volume

CH4 50

VOCs 2

O2 7

N2 37

CO2 4

For underground mining, methane collection of some of the gas is feasible and is required as a 

safety precaution. The gas is collected as a combustible fuel gas containing variable proportions 

of air up to 65 percent. The remainder of the released gas is removed from the mine in 

ventilation air in which the methane content is kept below 0.5 percent. The fraction of the total 

methane gas that can be recovered as a combustible fuel gas is site specific. For the purpose of 

this study, it is assumed that 60 percent of the methane released can be collected as combustible 

gas and burned usefully to generate power or is flared7.

On the basis of the analysis of mine methane and 60 percent collection, the atmospheric 

emissions of unburned gas associated with the production of one tonne of clean coal is 2.86 kg of 

CH4 and 0.63 kg of CO2.
Storing Captured Co2—Basis & Approach

3-6

If it is assumed that all of the collected methane is sufficiently consistent in quality to be burned 

in an open-cycle gas turbine operating at 33 percent efficiency for power generation, then 

60 percent of the power requirements of the mine (38 kWh/tonne of coal) would be generated.

The balance of the power would be generated from natural gas. The emissions will be the 

products of combustion of 11.2 kg of CH4 and 0.50 kg of VOCs per tonne of clean coal. This 

combustion will yield 33.2 kg of CO2 per tonne of coal. Emissions of CH4 and N2O from 

burning mine gas are calculated based on the EPA emission factors for gas turbines resulting in 

emissions of CH4 of 3.4x10-3lb/tonne of coal and N2O of 1.2x10-3 lb/tonne of coal.8

3.4.2 Emissions from Rail Transport

The fuel consumption for rail transport of coal in the U.S. is 0.25 MJ/tonne-kilometer.9 For the 

IGCC case, based on the transport over 100 km of 1.01 million tonnes per year of U.S. coal, the 

total annual fuel energy consumption would be 25.36 TJ corresponding to 571 tonnes per year of 

diesel oil. The corresponding emissions, based on carbon content of 87 percent, would be 

1821 tonnes per year of CO2. Other emissions are calculated from EPA emission factors for 

locomotives.10

3.4.3 Emissions from the Operation of the Power Plant

The power plant data for IGCC is based on CO2 abatement by shift conversion and scrubbing of 

CO2 from the fuel gas prior to the combustion of hydrogen in a gas turbine. The CO2 emissions 

are derived from the mass balance data reported in Reference 1. N2O content of the IGCC gas 

turbine exhaust is estimated to be 0.5 ppm.6 The N2O emissions were calculated from the design 

flow rate of stack gas of 510 Nm3/s, which is equivalent to 12.9x109 Nm3/year. Since there are 

no hydrocarbons in the gas turbine fuel, the gas turbine exhaust will be free from methane.

3.4.4 Emissions from the Construction and Decommissioning of the Power Plant

The bulk construction materials required for power plants are essentially steel, cement, and 

aggregate. The energy requirements of these commodities are reported as about 22, 5, and 

0.12 GJ/tonne respectively.6,11 The amount of material required for the construction of power 

plants is broadly proportional to the size and complexity of the power plant, which is in turn 

proportional to its cost. Analysis of the data available6 showed typical steel, cement, and 

aggregate consumption in the ratio 1:1:6; therefore, the overall construction energy requirement 

for a power plant is estimated to be about 28 GJ/tonne of steel used in the plant.

Analysis of plant cost information6 indicates a steel utilization in power plant projects of the 

order of one tonne of steel per $50,000 of total investment. On the basis of this rule of thumb, 

the energy requirement for construction of the IGCC power plant is estimated to be about 

404,000 GJ. Other energy consumption on the construction site is assumed to be trivial in 

comparison with the energy requirement of the construction materials.
Storing Captured Co2—Basis & Approach

3-7

To determine the emissions associated with the energy expended in making the steel and cement, 

it is assumed that gas will be used to supply the energy for making construction materials. The 

total energy consumption in construction corresponds to 1.5 percent of the annual energy 

consumption of the power plant. The associated emissions are assumed to be equivalent to the 

emissions from natural gas combustion. 12 In addition, 0.4 tonnes of CO2 is emitted from the 

manufacture of one tonne of cement. Therefore, an additional 6,000 tonnes of CO2 arises from 

this source.

The decommissioning of the power plant at the end of its useful life will involve some 

expenditure of energy depending on the use that is made of the site. There will be some 

recycling of steel from the decommissioning operation that is a less energy intensive operation 

than the making of new steel; therefore, an energy credit could accrue. For the purpose of this 

study, it is assumed that there will be a net energy consumption for decommissioning equal to a 

nominal 10 percent of the energy consumed in the construction of the power plant. Emissions 

for decommissioning are, therefore, calculated on the basis of 10 percent of the emissions from 

the construction of the power plant.

For the purpose of the development of a single emissions inventory, the emissions associated 

with power plant construction and decommissioning are presented as if the emissions were 

produced continuously over the 20-year lifetime of the plant.

3.4.5 Emissions from the Compression of CO2

Emissions for the power used for compression are based on the power plant’s life cycle 

emissions. The emissions from the construction and decommissioning of the CO2 compression 

unit located at the power plant were based on the same assumptions as were used for the power 

plant.

3.4.6 Emissions from the Transportation of CO2

The amount of steel for the pipeline construction was based on the amount of steel needed for the 

size pipe used, 97.3 kg per m (65.4 lb per ft) for the 12-inch pipe. The emissions from the 

construction and decommissioning of the pipeline were based on the same energy assumptions 

(28 GJ/tonne of steel used) as were used for the power plant.

3.4.7 Emissions from the CO2 Storage Options

Emissions for the power used for storage are based on the power plant’s life-cycle emissions. 

The emissions from the construction and decommissioning of the CO2 storage systems were 

based on the same assumptions as were used for the power plant. In the case of ocean storage 

via tanker, it was also necessary to consider the CO2 emitted by the tankers and due to boil off. 

The methodology used to calculate the CO2 emitted from these sources is described in 

Section 9.4.3.
Storing Captured Co2—Basis & Approach

3-8

Table 3-3

Greenhouse gas life-cycle analysis

With CO2 Capture (Case 3a)

IGCC Plant

CO 2 N 2 0 CH 4

Coal Mine 33,698 0.5 1.6

Mine Methane Vented 639 minor 2,901

Coal Train 1,821 minor minor

Power Plant Emissions 205,812 11.0 minor

Power Plant Construction 1,243 0.0 0.0

Power Plant Decommissioning 124 0.0 0.0

Total Power Plant 243,338 12 2,903

100 Year Multiplier 1 296 23

Total Power Plant CO2 Equivalent 243,338 3,433 66,762

Total (mg CO2 Equivalent / kWh) 86,053 1,214 23,609

Power Plant Total (mg CO 2 Equivalent / kWh ) 110,876

Compression of Captured CO 2 to Pipeline Spec

CO2 Compression

Compression Power 2,650 kW 1,598 0.1 19

Construction 6.1 0.0 0.1

Decommissioning 0.6 0.0 0.0

Total 1,605 0.1 19

100 Year Multiplier 1 296 23

CO2 Equivalent 1,605 23 441

CO2 Compression Total (CO 2 Equivalent ) 2,068

Transportation & Sequestration of Captured CO 2

CO2 Transportation

Pipeline Construction 913 0.0 0.0

Pipeline Decommissioning 91 0.0 0.0

Total 1,005 0 0

CO2 Sequestration-EOR

Power 25,142 kW 128,378 1 155

Plant Construction

 Aboveground Equipment 279 0.0 0.0

 Subsurface Equipment 64 0.0 0.0

Plant Decommissioning 34 0.0 0.0

Total 128,755 1 155

CO2 Transportation & Sequestration Total 129,759 1 155

100 Year Multiplier 1 296 23

CO2 Equivalent 129,759 210 3,569

Transportation & Sequestration Total (CO 2 Equivalent) 133,538

GHG Emissions

tonnes/year
Storing Captured Co2—Basis & Approach

3-9

3.5 References

1 “Enhanced Coal Bed Methane Recovery with CO2 Sequestration.” IEA Greenhouse Gas 

R&D Programme. Report Number PH3/3, August 1998.

2 “1998 Joint Association Survey on Drilling Costs.” American Petroleum Institute, Policy 

Analysis and Statistics Department, November 1999. http://www.api.org/axsapi/products/joint.htm.

3 “Costs and Indices for Domestic Oil and Gas Field Equipment and Production Operations.”

Energy Information Administration, Office of Oil and Gas, March 2000.

http://www.eia.doe.gov/oil_gas/natural_gas/data_publications/cost_indices/c_i.html.

4 “A Cost Model for Transport of Carbon Dioxide.” The Energy Laboratory, Massachusetts 

Institute of Technology, January 2000.

5 “Evaluation of Innovative Fossil Fuel Power Plants with CO2 Removal.” EPRI Report 

No. 1000316, Interim Report, December 2000. Cosponsors: U.S. Department of

Energy/Office of Fossil Energy and U.S. Department of Energy/NETL, EPRI Project

Manager N. A. H. Holt.

6 “Full Fuel Cycle Study on Power Generation Schemes Incorporating the Capture and 

Disposal of Carbon Dioxide.” ETSU, United Kingdom, October 1994.

7 “Methane Emissions from Coal Mining.” IEA GHG Report PH2/5, June 1996.

8 Gas Turbine Emission Factors. http://www.epa.gov/ttn/chief/ap42/ch03/final/c03s01.pdf.

9 Khoury. “Coal Cleaning Technology.” Edited by D. L. Khoury, Noyes Data Corporation, 

1981.

10 Locomotive Emission Factors. http://www.epa.gov/oms/invntory/r92009.pdf.

11 Boustead, I., and G. F. Hancock. “Handbook of Industrial Energy Analysis.” Wiley and 

Sons, 1979.

12 Gas Combustion Emission Factors, 

http://www.epa.gov/ttn/chief/ap42/ch01/final/c01s04.pdf.

4-1

4

PIPELINE TRANSPORT

4.1 Introduction

This chapter looks at the transport of CO2 via pipeline from the base case IGCC power plant to 

the injection site, for the geologic storage options, and the shoreline collection point, in the case 

of the ocean storage options.

4.2 State of the Art

Over 110 million standard cubic meters (scm) per day of CO2 are transported by pipeline in the 

United States, frequently for distances greater than 100 km. Details of currently operating CO2

pipelines in the United States are given in Table 4-1.

1,2,3,4,5,6
Pipeline Transport

4-2

Table 4-1

CO2 pipelines in the United States

Name Operator Route CO2 Source Length

(km)

Diameter (in) Capacity (106

scm per day)

Cortez Pipeline2Kinder Morgan 

CO2

McElmo Dome 

to Denver City 

CO2 Hub

Natural CO2

deposit

311 30 28

McElmo Creek 

Pipeline2

ExxonMobil McElmo Dome 

to McElmo 

Creek Unit (UT)

Natural CO2

deposit

25 8 2

Bravo Pipeline2BP Bravo Dome to 

Denver City CO2

Hub

Natural CO2

deposit

135 20 11

Sheep Mountain 

I

2

BP Sheep Mountain 

Field to 

Rosebud 

connection with 

Bravo Dome

Natural CO2

deposit

114 20 9

Sheep Mountain 

II2

BP Rosebud 

connection to 

Denver City CO2

Hub and onward 

to Seminole San 

Andres Unit (TX)

Natural CO2

deposit

139 24 14

Central Basin 

Pipeline

2

Kinder Morgan 

CO2

Denver City CO2

Hub to 

McCamey, TX

- - 26,16 17

Este Pipeline2ExxonMobil Denver City CO2

Hub to Salt 

Creek, TX

- 74 12,14 7

Slaughter 

Pipeline2

ExxonMobil Denver City CO2

Hub to Hockley 

County, TX

- 25 12 5

West Texas 

Pipeline2

Trinity Pipeline Denver City CO2

Hub to Reeves 

County, TX

- 79 12,8 3
Pipeline Transport

4-3

Table 4-1 (continued)

CO2 pipelines in the United States

Name Operator Route CO2 Source Length

(km)

Diameter (in) Capacity (106

scm per day)

Llano Lateral 

Pipeline2

Trinity Pipeline runs off Cortez 

main line to 

Llano, NM

Natural CO2

deposit

33 12,8 3

Canyon Reef 

Carriers 

Pipeline2

Kinder Morgan 

CO2

McCamey, TX to 

SACROC field

- 87 16 7

Val Verde 

Pipeline5, 6

PSCC connects 

Mitchell, Gray 

Ranch, Pucket 

and Terrell gas 

processing 

facilities to 

Canyon Reef 

Carriers main 

line

Gas processing 

facilities

51 10 4

Weyburn 

Pipeline3

Dakota 

Gasification 

Company

Great Plains 

Synfuels plant 

(Beulah, ND) to 

Weyburn field 

(Saskatchewan, 

Canada)

Coal gasification 

plant

127 14,12 3

Choctaw 

Pipeline4

Denbury 

Resources

Jackson Dome 

to Bayou 

Choctaw Field, 

LA 

Natural CO2

deposit

115 20 6

1 in = 0.0254m
Pipeline Transport

4-4

Transported CO2 is most commonly used for enhanced oil recovery (EOR). The use of CO2 for 

EOR is a proven technology with 72 CO2 floods in the United States.7 Most of these floods are 

dependent upon naturally occurring CO2, which is obtained from high-pressure, high-purity 

underground deposits. The most important of these natural CO2 deposits, in decreasing order of 

current production, are the McElmo Dome, the Bravo Dome, the Sheep Mountain Field and the 

Jackson Dome.4,8 A small fraction of the CO2 supply comes from anthropogenic sources, 

including the Mitchell, Gray Ranch, Pucket and Terrell gas processing facilities in the southern 

Permian basin and the Great Plains coal gasification plant at Beulah, North Dakota. 3, 6

The operation of the Canyon Reef Carriers pipeline, one of the first CO2 pipelines constructed 

for EOR, provides a reference for future CO2 handling systems. Put into operation in 1972, it 

recorded only five failures (with no injuries) during its first twelve years of operation. Two 

failures were explosions at compressor stations that resulted from air (oxygen) being drawn into 

the suction line from the extraction plant stack line. In order to rectify the problem, the 

emergency shutdown system was adjusted so that the loss of positive pressure on the suction line 

would cause the compressors to come to an immediate halt. The three other failures were 

ruptures at the injection station due to localized ‘hot spots’ in the tubes of the direct-fired line 

heater. The first was attributed to the build-up of a corrosion product in a pipe that took place 

before its installation and was not removed by initial cleaning. The other two ruptures occurred 

near support brackets where the distribution of flow through the parallel tube arrangement was 

not equal. Provisions for better temperature monitoring and flow distribution in the heater were 

put in place to prevent further such accidents.9

An important technical consideration in the design of pipelines for transport of supercritical CO2

is that the CO2 remains above critical pressure. This can be achieved by means of 

recompression of the CO2 at certain points along the length of the pipeline. Recompression is 

often needed for pipelines over 150 km (90 miles) in length. It is important to note, however, 

that recompression may not be needed if a sufficient pipe diameter is used. For example, the 

Weyburn CO2 pipeline runs for 330 km (205 miles) from North Dakota to Saskatchewan, 

Canada, without recompression. 10

A survey of North American pipeline project costs yields several pertinent observations. First, 

for a given pipeline diameter, the per unit distance cost of construction is generally lower the 

longer the pipeline. Second, pipelines built nearer populated areas tend to be more expensive. 

Finally, road, highway, river, or channel crossings and marshy or rocky terrain also greatly 

increase the cost.11

4.3 Process Description

The CO2 for pipeline transport is taken from Case 3a of the DOE/EPRI Report on CO2 removal 

from fossil fuel power plants.12 This case is used for the design basis since potential CO2 sources 

from a coal-based power plant would most probably be associated with an IGCC plant. CO2

recovery from IGCC is most economical because of the CO2 concentration in syngas at a high 

partial pressure, enabling the use of conventional recovery processes. The pipeline is to be 

designed to handle 3.76 million scm (7,389 tonnes) of CO2 per day. It is important to note that, 
Pipeline Transport

4-5

since the capacity factor of the IGCC power plant is assumed to be 80 percent, this CO2 is only 

supplied 80 percent of the time.

The pipeline design must conform to the United States Department of Transportation (DOT) 

Codes 49 CFR 195, Transportation of Hazardous Liquids by Pipeline, and 49 CFR 192, 

Transportation of Natural and Other Gas by Pipeline: Minimum Federal Safety Standards.

4.4 Methodology Used

The first stage of the CO2 storage process involves the CO2 being transported via pipeline from 

the base case IGCC plant to the injection site, for the geologic storage options, and to the 

shoreline collection point, in the case of the ocean storage options. Overland distances of 100 

and 300 km, for the base and sensitivity cases respectively, are considered. The method used to 

calculate the cost of CO2 pipeline transport can be broken down into a couple of steps. First, the 

diameter of the pipeline is calculated. Next, based on the calculated diameter, the capital and 

O&M costs as well as the total cost per tonne of CO2 are found. An overview of the cost model 

is given in Figure 4-1. The diameter and cost calculations are explained in greater detail below.

Inputs:

CO2 mass flow rate

Pipeline length

CO2 inlet pressure

CO2 outlet pressure

Capital charge rate

Outputs:

Total capital cost

Total O&M cost

Total annual cost 

Total cost per tonne CO

TRANSPORT MODEL

Internal Calcs:

CO2 density

CO2 viscosity

Pressure drop per unit length

Pipe diameter

Figure 4-1

Pipeline transport cost model overview diagram

4.4.1 Diameter Calculation

The pipeline inlet CO2 pressure is set equal to 152 bar, which is equivalent to the pressure of the 

CO2 supplied by the base case IGCC plant. Based on a recommendation that the pipeline CO2

pressure not be allowed to fall below 103 bar,13 this latter value is used for the pipeline outlet 

CO2 pressure. The maximum allowable pressure drop per unit length (ΔP/ΔL) is found as the 

difference between the pipeline inlet and outlet CO2 pressures divided by the pipeline length.

Next, based on an assumed ambient temperature of 25oC, the CO2 density and viscosity are 

calculated. The CO2 density (ρ) is calculated to be 884 kg/m3, using a correlation based on data 

from the National Institute of Standards and Technology (NIST) for a temperature range of 5 to 
Pipeline Transport

4-6

27oC and a pressure range of 80 to 140 bar.14 The CO2 viscosity (μ) is found, from a correlation 

published by Nihous and Bohn, 15 to be 6.06 x 10-5 N-s/m2.

The pipeline diameter is calculated using the equations for pressure drop and head loss due to 

frictional resistance in a pipe, assuming turbulent flow. This calculation uses an iterative 

procedure, which initially requires that the diameter be guessed. This guessed value is used to 

find the Reynolds number (Re) given by

Re = 4m/πμD

where m is the CO2 mass flow rate and D is the pipeline diameter. Based on this calculated 

Reynolds number and a roughness factor of 0.00015,16 the Fanning friction factor (f) is then 

found using an empirical relationship based on the Moody chart. Combining the equations for 

pressure drop and head loss gives the simplified formula

D

5

 = 32fm2/π

2

ρ(ΔP/ΔL)

from which the diameter is determined. This calculated value of diameter is then used for the 

next iteration, and so on. 1 in = 0.0254m

Figure 4-2 gives the diameter, calculated for the base case, as a function of CO2 mass flow rate.

Diameter as a Function of CO2 

Mass Flow Rate

0

5

10

15

20

25

30

35

40

0 20 40 60

Mass flow rate (Mt CO2/y)

Pipe diameter (in)

1 in = 0.0254m

Figure 4-2

Diameter for the base case as a function of CO2 mass flow rate
Pipeline Transport

4-7

4.4.2 Cost Calculations

The amount of cost data on CO2 pipelines in the open literature is very limited, but there is an 

abundance of cost data for natural gas pipelines. For this reason, land construction cost data for 

natural gas pipelines were used to estimate construction costs for CO2 pipelines. This is 

adequate given that there is little difference between land construction costs for these two types 

of pipeline.8 It is worth noting, though, that CO2 pipelines might be slightly more expensive 

because of the greater wall thickness needed to contain the CO2, which is transported at higher 

pressures.

The cost data found for natural gas pipelines consists of cost estimates filed with the United 

States’ Federal Energy Regulatory Commission (FERC), and reported in the Oil and Gas 

Journal. 11,17 Figure 4-3 gives the breakdown of costs on a dollar per mile basis for four pipeline 

diameters: 8, 16, 24 and 30 inches (0.20, 0.41, 0.61, and 0.76 m). Costs are broken down into 

material, labor, right-of-way (ROW) and miscellaneous components. Materials can include line 

pipe, pipe coating, cathodic protection and telecommunications equipment. Right-of-way costs 

include obtaining the right-of-way and allowing for damages. Miscellaneous costs generally 

cover surveying, engineering, supervision, contingencies, allowances for funds used during 

construction, administration and overheads, and regulatory filing fees.

Average Land Construction Costs of Natural Gas 

Pipelines

$21,085 $42,630 $36,890 $46,278

$102,422

$203,676

$319,358

$126,855

$230,983

$434,516

$382,936

$68,721

$81,386

$170,290

$182,798

$70,255

0

250,000

500,000

750,000

1,000,000

8 16 24 30

Pipe diameter (in)

Average cost ($/mi)

Misc. 

Labor

Material

ROW

1 in = 0.0254m, 1 mi = 1.61 km

Figure 4-3

Breakdown of pipeline cost on a dollar per mile basis 

A breakdown of costs on a percentage of total cost basis is given in Figure 4-4. The graph 

suggests that right-of-way costs can be estimated at 5% of total costs, while labor, material, and 
Pipeline Transport

4-8

miscellaneous costs appear to be random percentages of total costs. It is also important to note 

that each of these costs is independent of pipeline diameter.

Average Land Construction Costs of Natural Gas 

Pipelines 

7.0% 9.3%

4.4% 5.0%

23.5% 22.4%

24.1%

34.3%

42.4%

50.5%

51.4%

41.1%

22.9%

17.8% 20.1% 19.6%

0%

20%

40%

60%

80%

100%

8 16 24 30

Pipe diameter (in)

%Misc.

%Labor

%Material

%ROW

1 in = 0.0254m

Figure 4-4

Breakdown of pipeline cost on a percentage of total cost basis 

Total costs in dollars per mile are plotted against pipeline diameter in Figure 4-5. A regression 

line fit to this data yields a pipeline construction cost of $20,989/in/km ($33,853/in/mile). 

According to an industry expert13 the construction cost for CO2 pipelines should be close to 

$12,400/in/km ($20,000/in/mile). One possible reason for this lower CO2 pipeline construction 

cost estimate is that CO2 pipelines are currently constructed in sparsely populated areas. Another 

is that the rock in New Mexico and West Texas where most CO2 pipelines have been laid is easy 

to dig in. It is important to note that neither cost figure includes recompression costs.
Pipeline Transport 

4-9 

Land Construction Cost Data for Natural Gas Pipelines

1989-1998

y = 33853x

R2

 = 0.9363

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

0 5 10 15 20 25 30 35 40

Pipe diameter (in)

Average cost ($/mi)

Error bars represent one 

standard deviation of cost 

data over a period of 10 

years for a given 

diameter.

1 in = 0.0254m, 1 mi = 1.61 km

Figure 4-5

Regression analysis of pipeline construction cost data 

It has been reported that it costs about $40,000 to $60,000 per month to operate 480 km 

(300 miles) of pipeline and that this figure should be doubled to account for associated overhead 

costs.13 Taking the higher value to be on the conservative side, O&M costs are estimated to be 

$3,100/km ($5,000/mile) per year, independent of pipeline diameter. It should be noted that this 

O&M cost estimate does not account for pumping or its associated costs. 

Total pipeline construction cost is found using the $20,989/in/km ($33,853/in/mile) cost factor. 

Applying the O&M cost factor of $3,100/km ($5,000/mile), gives the respective total O&M 

costs. Finally, the total annual cost per tonne of CO2 is found by annualizing the construction 

cost using a capital charge rate of 15 percent per year and adding this to the annual O&M cost. 

Figure 4-6 shows the cost per tonne of CO2, calculated for the base case, as a function of CO2

mass flow rate. 
Pipeline Transport

4-10

Total Annual Cost

Construction and O&M

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

0 10 20 30 40 50 60

Mass flow rate (Mt CO2/yr)

Cost ($/t CO2

)

Figure 4-6

Total cost per tonne of CO2 for the base case as a function of CO2 mass flow rate

4.5 Design Basis

4.5.1 Pipeline Design

The methodology described in Section 4.4.1 was used to determine pressure drop per unit length 

and pipeline diameter for the base and sensitivity cases. The design bases for pipeline transport 

are summarized in Table 4-2.

Table 4-2

Design bases for pipeline transport

Parameter Unit Pipeline Transport 

Base Case

Pipeline Transport 

Sensitivity Case

Pipeline Length km 100 300

CO2 Inlet Pressure MPa 15.2 15.2

CO2 Outlet Pressure MPa 10.3 10.3

Pressure Drop per 

Unit Length*

Pa/m 49 16

Pipe Diameter* inches 11.2 13.8

Nominal Pipe Size* inches 12 16

* calculated

1 in = 0.0254m
Pipeline Transport

4-11

4.5.2 Capital and O&M Cost Inputs

The capital and O&M costs of the pipeline, for the base and sensitivity cases, were calculated 

using the methodology described in Section 4.4.2. Table 4-3 shows the results.

Table 4-3

Capital and O&M cost inputs for the pipeline transport base and sensitivity cases

Parameter Unit Pipeline Transport 

Base (100 km) Case

Pipeline Transport 

Sensitivity (300 km) 

Case

Pipe Diameter inches 11.2 13.8

Capital Cost $ 23,500,000 87,100,000

O&M Cost $ 310,000 930,000

The total cost of constructing the pipeline is $23.5 and $87.1 million for the 100 and 300 km 

cases, respectively. The construction cost of the 300 km pipeline is more than three times the 

cost of the 100 km pipeline due to the fact that a larger diameter pipe is required, i.e. a 16-inch as 

opposed to 12-inch pipe.

4.6 MIT Model Results

Based on the model developed by MIT, the respective values of total cost per tonne of CO2 for 

the base and sensitivity cases are $1.78 and $6.49. These are converted to CO2 equivalent LC 

GHG avoided bases in Chapters 5-9 when they are combined with the storage concepts.

4.7 Comparison to Literature

4.7.1 Studies Used in Model Evaluation

Data related to overland pipeline transport of CO2 were taken from the studies listed in 

Table 4-4.
Pipeline Transport

4-12

Table 4-4

Overland pipelines’ characteristics

Study CO2 flow 

rate (Mt/yr)

Initial CO2

pressure 

(bar)

Diameter 

(m)

Length 

(km)

Recompression 

station 

included

IEA aquifer18 3.90 208 0.400 30 No

IEA depleted 

reservoir18

3.16 110 0.400 50 No

British Coal19 3.63 136 0.350 425 Yes

Weyburn10 2.00 170 0.305 127 No

A pumping station is required for the ‘British Coal’ CO2 pipeline, of which only the onshore 

section is considered here, due to its extreme length. For the purpose of comparing the capital 

cost of this pipeline with that determined by the model, the pumping station was ignored. It 

should also be noted that cost data were not available for the ‘Weyburn’ pipeline.

4.7.2 Comparison of Values from Model and Studies

The graph below in Figure 4-7 shows pipe diameter as a function of CO2 mass flow rate. Also 

shown in the figure, is the value of pipe diameter, for a specific CO2 mass flow rate, given in 

each of the four studies. The model’s calculation of pipe diameter can be seen to strongly agree 

with this studies’ estimate. 
Pipeline Transport 

4-13 

Diameter as a Function of CO2 Mass Flow Rate

0

5

10

15

20

25

30

35

40

0 10 20 30 40 50 60

Mass flow rate (Mt CO2/yr)

Pipe diameter (in)

Transport Model (Base Case)

IEA aquifer study

IEA depleted reservoir study

British Coal

Weyburn

Figure 4-7 

Comparison of pipe diameter values 

Figure 4-8 shows the estimated capital costs of the pipeline versus mass flow rate, and a 

comparison with three other studies. Our model shows generally lower costs, especially when 

compared to the ‘British Coal’ study. The discrepancy can be attributed to the additional costs 

associated with pipelines located in the more populated areas of Europe as opposed to North 

America. 
Pipeline Transport 

4-14 

Construction or Capital Cost

0

20

40

60

80

100

120

0 10 20 30 40 50 60

Mass flow rate (Mt CO2/yr)

Cost ($M/100 km)

Transport Model (Base Case)

IEA aquifer study

IEA depleted reservoir study

British Coal

Figure 4-8 

Comparison of pipeline capital cost values 
Pipeline Transport 

4-15 

4.8 References 

1 Moritis, G., “Future of EOR & IOR: New companies, infrastructure, projects reshape 

landscape for CO2 EOR in U.S.,” Oil & Gas Journal, May 14, 2001. 

2 Kinder Morgan CO2 Company, “CO2 Transportation,” [Online document], 2001, [cited Apr. 

2002], Available HTTP: 

http://www.kne.com/co2/transport.cfm 

3 International Energy Agency Greenhouse Gas R&D Programme (IEAGHG), “Weyburn 

CO2-EOR Project,” [Online document], 2001, [cited Apr. 2002], Available HTTP: 

http://www.ieagreen.org.uk/weyburn4.htm 

4 Stevens, S.H., J.M. Pearce and A.A.J. Rigg, “Natural analogs for geologic storage of CO2: 

An integrated global research program,” presented at the First National Conference on 

Carbon Sequestration, Washington D.C., May 15-17, 2001. 

5 Petro Source Corporation, “Val Verde CO2 pipeline completes construction and begins 

operations,” [Online document], 1998, [cited Apr. 2002], Available HTTP: 

http://www.petrosource.com/news_ValVer.htm 

6 “Enhanced Oil Recovery Scoping Study,” EPRI, Palo Alto, CA. Tech. Rep. 113836, 1999. 

7 “OGJ Special – Worldwide EOR survey 2000,” Oil & Gas Journal, pp. 44-61, Mar. 20, 

2000. 

8 Kinder Morgan CO2 Company, “CO2 Supply,” [Online document], 2001, [cited Jan. 2002], 

Available HTTP: 

http://www.kne.com/co2/supply.cfm 

9 Gill, T.E., “Canyon Reef Carriers Inc. CO2 pipeline: Description of 12 years of operation,” 

presented at the ASME Pipeline Engineering Symposium, 1985. 

10 Hattenbach, R.P., Wilson, M. and K. Brown, “Capture of carbon dioxide from coal 

combustion and its utilization for enhanced oil recovery,” in Greenhouse Gas Control 

Technologies, Elsevier Science, New York, 1999, pp. 217-221. 

11 True, W.R., “Weather, construction inflation could squeeze North American pipelines,” Oil 

& Gas Journal, Aug. 31, 1998. 

12 "Evaluation of Innovative Fossil Fuel Power Plants with CO2 Removal," EPRI Report No. 

1000316, Interim Report, December 2000, Cosponsors: U.S. Department of Energy/Office 

of Fossil Energy and U.S. Department of Energy/NETL, EPRI Project Manager N. A. H. 

Holt. 

Available HTTP: http://www.netl.doe.gov/coalpower/gasification/pubs/pdf/EpriReport.pdf 
Pipeline Transport 

4-16 

13 Fox, Chuck, e-mail communication, Dec. 30, 1999. 

14 Herzog, H., “Conceptual design of pipeline operations and control”. 

15 Nihous and Bohn, “Principles of heat transfer”. 

16 Perry, R.H. and D.W. Green, “Perry’s Chemical Engineers’ Handbook – Seventh Edition,” 

1997. 

17 True, W.R., “U.S. gas pipelines improve operations, want to expand,” Oil & Gas Journal, 

Nov. 26, 1990. 

18 Ormerod, W., “The disposal of carbon dioxide from fossil fuel fired power stations,” IEA 

Greenhouse R&D Programme, Cheltenham. Tech. Rep. IEAGHG/SR3, Jun. 1994. 

19 Summerfield, I.R., S.H. Goldthorpe, N. Williams and A. Sheikh, “Costs of CO2 disposal 

options,” Energy Convers. Mgmt, vol. 34, no. 9-11, pp. 1105-1112, 1993. 
5-1

5

ENHANCED OIL RECOVERY

5.1 Introduction

This chapter deals with the storage of CO2 in depleted oil reservoirs where enhanced oil 

production results in a value-added product. The use of depleted oil reservoirs for CO2 storage 

without enhanced production is treated separately in Chapter 7.

5.2 State of the Art

5.2.1 Applications

There were a total of 84 commercial or research-level applications of enhanced oil recovery 

using CO2 floods (CO2-EOR) worldwide in 2000. The amount of enhanced oil production from 

these CO2-EOR projects during that year averaged 200,772 barrels (bbl) of oil per daya, which is 

only a very small amount (0.3 percent) of that year’s total worldwide oil production of 

67.2 million bbl of oil per day. The United States account for 72 of the 84 projects, or 96 percent 

of worldwide enhanced oil production from CO2 floods, and is as such the world leader in the 

use of CO2-EOR technology. Currently, Turkey is the only other country with a commercialscale application of CO2-EOR, with Canada and Trinidad having only pilot-scale projects.1,2

Enhanced oil production from the 72 CO2 floods in the United States in 2000 was 192,209 bbl of 

oil per day, which is equivalent to 5 percent of total U.S. oil production during the same period. 

Most of these CO2 floods (53) are located in the southwestern United States within the Permian 

basin of western Texas and eastern New Mexico. The next largest concentrations of CO2 floods 

in the United States are in the Rocky Mountain and Mid-continent regions. The details of the six 

largest CO2-EOR projects in 2000 are given in Table 5-1. It should be noted that these six 

projects are all situated in the United States and that together they accounted in 2000 for 

47 percent of worldwide enhanced oil production from CO2 floods.1,2

 

a

 Only the oil recovered due to the CO2 flood is included here as enhanced oil production. Quoted enhanced oil 

production figures may thus account for only a fraction of the total amount of oil produced during the tertiary 

recovery process.
Enhanced Oil Recovery

5-2

Table 5-1

Six largest CO2-EOR projects1

Operator Field Basin/Region Area

(km2)

Production 

Wells

Injection 

Wells

EOR 

Production 

(bbl/d)

Altura Wasson 

(Denver)

Permian 113 735 385 29,000

Amerada 

Hess

Seminole 

(Main)

Permian 64 408 160 25,900

Chevron Rangely 

Weber Sand

Rocky 

Mountain

61 341 209 11,208

ExxonMobil Salt Creek Permian 49 137 100 9,300

Devon 

Energy

SACROC Permian 202 325 57 9,000

Altura Wasson (ODC) Permian 32 293 290 9,000

Currently, there is no commercial-scale CO2-EOR project that utilizes CO2 from a power plant. 

In the 1980s, there were three small-scale CO2-EOR projects that utilized CO2 from gas boiler 

power plants. These plants were shut down when the price of oil dropped in the mid-1980s, 

making this source of CO2 too expensive.3 The main obstacle to the utilization of this source of 

CO2 for EOR is its significant cost of capture. Most of the CO2-EOR projects, particularly those 

located in the Permian basin, are dependent upon naturally occurring CO2, which is obtained 

from high-pressure, high-purity underground deposits. The most important of these natural CO2

deposits, in decreasing order of size, are the McElmo Dome, the Bravo Dome, and the Sheep 

Mountain Field.4 A small fraction of the Permian basin CO2 supply has also come from 

anthropogenic sources, namely the Mitchell, Gray Ranch, Pucket, and Terrell gas processing 

facilities in the southern Permian basin. In contrast, the Rocky Mountain and Mid-continent 

regions are almost wholly supplied by anthropogenic CO2 from gas processing and fertilizer 

production facilities. The Rangely Weber Sand CO2-EOR project, for example, is supplied by 

the La Barge gas processing plant in southwestern Wyoming and is as such the world’s largest 

single sequestration site of anthropogenic CO2.

5

5.2.2 Storage Potential

The Weyburn Field in southeastern Saskatchewan, Canada, is the only CO2-EOR project to date 

that has been monitored specifically to understand CO2 sequestration. In the case of most CO2-

EOR projects, much of the CO2 injected into the oil reservoir should be considered as being only 

temporarily stored. This is because the decommissioning of an EOR project usually involves the 

“blowing down” of the reservoir pressure to maximize oil recovery. This “blowing down” 

results in CO2 being releasedb, with a small but significant amount of the injected CO2 remaining 

 

b

 The CO2 from ‘blow down’ may be either vented or reused in other EOR fields.
Enhanced Oil Recovery

5-3

dissolved in the immobile oil. In the case of the Weyburn Field, no “blow-down” phase is 

planned, thereby allowing for permanent CO2 sequestration. Over the anticipated 25-year life of 

the project, it is expected that the injection of some 18 million tonnes of CO2 from the Dakota 

Gasification Facility in North Dakota will produce around 130 million bbl of enhanced oil. This 

has been calculated to be equivalent to, taking into account the CO2 emitted by the generation of 

electricity required, approximately 14 million tonnes of CO2 being prevented from reaching the 

atmosphere.5,6

5.2.3 Storage Mechanics

Most CO2 floods achieve enhanced oil production through miscible, as opposed to immiscible, 

displacement. The six largest CO2-EOR projects described above, for example, are all miscible 

CO2 floods. Miscible displacement involves the injected CO2 mixing thoroughly with the oil in 

the reservoir whereas, in the case of immiscible displacement, the CO2 remains physically 

distinct from the oil. The type of displacement that occurs is dependent on the reservoir pressure 

and crude oil composition, with a reservoir depth greater than 1,200 m and an oil density less 

than 22o API typically leading to miscible conditions. Miscible displacement leads to an ultimate 

recovery of about 7 to 15 percent of the original oil in place (OOIP). Immiscible displacement 

yields lower recoveries compared to miscible conditions, but can still achieve a high recovery 

rate due to oil swelling and viscosity reduction. Currently, only one large CO2-EOR project, 

located in Turkey, utilizes immiscible processes. However, it is expected that the number of 

immiscible CO2 floods will increase as the use of CO2-EOR becomes increasingly 

widespread.1,5,7,8,9

In CO2-EOR projects, it is most common for the CO2 to not be injected as a continuous gas 

stream, but for it to be alternated with water injection in a water-alternating-gas (WAG) process. 

This WAG process is carried out to help overcome the problem of high CO2 mobility that greatly 

reduces the effectiveness of CO2 flooding. This high CO2 mobility problem, caused by the CO2

having a lower density and viscosity than the reservoir oil, is responsible for the phenomena of 

gravity tonguing and viscous fingering. These phenomena are undesirable as they lead to 

injected CO2 flowing through areas that have already been swept. Taking advantage of the fact 

that water is less mobile than CO2, the WAG process is able to significantly improve the sweep 

efficiency through reducing CO2 mobility. This, in turn, results in improved oil recovery while 

also preventing early CO2 breakthrough in producing wells. The world’s largest CO2-EOR 

project, Wasson (Denver), is an example of a WAG flood.5,9,10

5.2.4 Feasibility of Storage Option

The use of CO2 floods for EOR presents a very attractive CO2 storage option. Even without CO2

sequestration credits, most of the active CO2-EOR projects are profitable. In addition to a valueadded product, CO2-EOR has the advantage that it has been widely applied and is a proven 

technology. Furthermore, significant advances continue to be made in the computer simulation 

of CO2 flood performance. This CO2 storage option also has the added bonus that most oil fields 

have already undergone primary and secondary recovery prior to CO2 flooding. This means that 

certain components of the existing infrastructure, such as the wells, are able to be simply adapted 
Enhanced Oil Recovery

5-4

for CO2 storage purposes. There is the downside that CO2 floods require significant additional 

infrastructure to handle the processing and recycling of CO2. On a positive note, however, the 

cost of anticorrosive equipment to deal with the problem of CO2 reacting with water to form 

carbonic acid has recently been reduced.5,7

5.3 Process Description

The CO2 for the EOR case is taken from Case 3a of the DOE/EPRI Report on CO2 removal from 

fossil fuel power plants.11 This case is used for the design basis since potential CO2 sources from 

a coal-based power plant would most probably be associated with an IGCC plant. CO2 recovery 

from IGCC is most economical because of the CO2 concentration in syngas at a high partial 

pressure, enabling the use of conventional recovery processes. The storage system is to be 

designed to handle 3.76 million scm (7,389 tonnes) of new CO2 per day. It is important to note 

that, since the capacity factor of the IGCC power plant is assumed to be 80 percent, this new CO2

is not available 20 percent of the time. Consideration of this issue is beyond the scope of the 

current project.

Figure 5-1 is a block flow diagram, indicating the overall flow and distribution of CO2 from the 

IGCC power plant to the EOR field. First, the CO2 leaving the plant is fed to an additional stage 

of compression to bring it up to the required pipeline inlet pressure. Second, the pipeline 

transports the CO2 a distance of 100 km to the EOR field, where it is mixed with recycled CO2

and injected into the EOR CO2 injection wells. Third, the oil produced at the EOR wells is 

separated from water and CO2 at the surface. Finally, the CO2 is dehydrated, compressed, and 

mixed with fresh incoming CO2.

The CO2 injection wells are an important component of the EOR field. These wells function as 

conduits for moving supercritical CO2 fluid from the surface down into the reservoir. The wells 

are regulated under the provisions of the Underground Injection Control (UIC) Program under 

the Federal Safe Drinking Water Act (SDWA) as either Class I or Class V wells.12

The EOR field also consists of a distribution system, which serves the following functions:

• Receives CO2 from the pipeline terminal and distributes it to the EOR CO2 injection wells.

• Gathers oil from the EOR production wells and delivers it to the tank battery.

• Compresses separated CO2 and mixes it with pipeline CO2 for injection into EOR CO2

injection wells.

The oil from the EOR production wells is carried by small pipelines called flow lines to a part of 

the production site known as the tank battery. In addition to storage tanks, the tank battery 

contains equipment for preparing the oil before further distribution. The fluid coming out of 

nearly all wells is actually a mixture of oil, gas (in this case CO2), salt water, and sediment. 

First, most of the CO2 present is separated from the oil and water at 7 bar, recompressed and 

recycled, then re-injected to help maintain reservoir pressure, and thereby, production. 

Separation of the remaining mixture is accomplished in special tanks where the settling process 
Enhanced Oil Recovery

5-5

separates water and oil, or it may be assisted by special equipment such as a heater treater. 

Vapor recovery units recover the remaining CO2, which is also recompressed and recycled.

Testing of the oil to determine its properties is conducted at the well site by taking samples of oil 

from the storage tanks. Today, oil volumes are measured with Lease Automatic Custody 

Transfer facilities (LACTs), which do most of the measuring, sampling, and testing without 

human intervention. Oil that has been completely prepared is stored in tanks at the well site until 

it is transported to the refinery.

Most CO2-EOR projects take place at fields that have already undergone secondary recovery, i.e. 

waterflooding. The modification of water-flooded fields for CO2 flooding involves:

• Makeover and equipping of injection wells

• Installation of CO2 distribution and recycle systems

• Provision of high-pressure injection equipment and related piping

• Replacement of selected production facilities

The production phase of the Weyburn field is expected to be more than 25 years. The 

Millennium Energy CO2 flood in West Texas has been going on since 1983. It is assumed that 

this flood has, like the power plant, a lifetime of 20 years. As a final note, the 

design/construction time is taken to be the same as the power plant, 4 years.
Enhanced Oil Recovery

5-6

Vapor 

Recovery

Compressor

84 kW (avg)

1 bar

7 bar

10 Producing Wells

110 kW

11 CO2Injection Wells

20 kW

Free Water

Separator

Sales Oil

400 barrels/day

Recycle

Compressor

240 kW (avg)

150 tonne/day 

(avg)

103 bar

CO2

Water

Water Injection 

Well

10/11 Well

EOR Module

Total

CO2Flow

CO2 Pipeline

Terminal

152 bar

Boost

Compressor

2,650 kW

100 km

Pipeline,

12” diameter

7,389 

tonne/day

83 bar

IGCC Plant

CO2Source

Block Flow Diagram: 

EOR

134 tonne/day

Figure 5-1

EOR block flow diagram
Enhanced Oil Recovery

5-7

5.4 Methodology Used

For specific projects, the complex interactions between the injected CO2 and reservoir oil would 

be modeled to assess the likely performance of a proposed CO2-EOR project. Based on the 

output of this modeling, the cost of the proposed CO2 flood is calculated. However, for our 

purposes of deve loping general costing algorithms, ‘rules of thumb’ are used to define the 

engineering parameters needed to estimate the cost of a CO2-EOR project. These ‘rules of 

thumb’ have been derived based on information from experts in the field and the literature.

The method used for costing the EOR process can be split up into a number of steps. First, the 

average amount of enhanced oil produced per day for the given CO2 mass flow rate is 

determined using a CO2 effectiveness factor of 170 standard cubic meters (6,000 standard cubic 

feet) of new CO2 per bbl of enhanced oil. Second, the number of production wells is found by 

dividing this total amount of enhanced oil produced per day by an assumed average of 40 bbl of 

enhanced oil per day being produced at each well. Third, a ratio of producers to injectors of 1 to 

1.1 is used to calculate the number of injection wells from the number of production wells. 

Fourth, the capital cost of the CO2 recycle plant is determined based on a maximum CO2 recycle 

ratio of 3, with an average recycle ratio of 1.1 being used for the plant’s O&M costs. Finally, the 

capital and O&M costs associated with the wells and the field equipment are calculated. Figure 

5-2 provides an overview of the cost model, with the assumptions made in each of these steps 

being discussed below in more detail.

5.4.1 CO2 Effectiveness

For the EOR-design basis, an average of 170 scm (6,000 scf) of CO2 is taken to remain in the 

ground for each bbl of enhanced oil production. It is important to note, however, that the 

effectiveness of CO2-EOR varies both from one basin to another and within a basin itself. In the 

case of the Permian basin, Malcolm Wilson from the Petroleum Technology Research Center 

indicated that around 170 to 227 scm (6,000 to 8,000 scf) of CO2 per bbl of enhanced oil would 

remain in the ground.13 In contrast, the CO2 effectiveness in the Weyburn Field, according to 

Ray Hattenbach from the Dakota Gasification Company, is closer to 85 scm (3,000 scf) per bbl 

of enhanced oil.14 Given this, it was deemed necessary that the sensitivity of the cost of EOR to 

a range of CO2 effectiveness values be determined. Based on the rough estimates given above, 

and the values given in the literature (see Table 5-2), a range of 85 to 227 scm (3,000 to 8,000 

scf) of CO2 per bbl of enhanced oil was chosen for the sensitivity calculation.

The CO2-EOR projects in Table 5-2 have illustrate the range of CO2 effectiveness. The projects 

chosen include two of the largest CO2 floods in the Permian basin. In addition, two other smaller 

CO2-EOR projects in this basin, namely Dollarhide (Devonian) and Vacuum, are provided as 

examples of CO2 floods displaying relatively high and low CO2 effectiveness, respectively. 

CO2-EOR projects located in the other two main CO2-flood regions are also included. These 

projects comprise the two largest CO2 floods in the Rocky Mountain region while, for the Midcontinent region, data was only available for two medium-sized floods. Finally, a last case study 

is made of the highly efficient CO2 flood at the Weyburn Field.
Enhanced Oil Recovery

5-8

COST MODEL

Internal Calcs:

Capital costs:

Site screening & evaluation, injection 

equipment, injection wells

O&M costs:

Normal daily expenses, surface 

maintenance, subsurface maintenance

Inputs:

Well depth

Capital charge rate

Outputs:

Total capital cost

Total O&M cost

Total annual cost 

Total cost per tonne CO2

Input:

CO2 mass 

flow rate

ENHANCED OIL 

PRODUCTION 

CALCULATION

Total enhanced oil production

PRODUCTION 

WELL NUMBER 

CALCULATION

INJECTION 

WELL NUMBER 

CALCULATION

Number of production wells

QUANTITY CO2

RECYCLED 

CALCULATION

Rule of thumb:

CO2 effectiveness

Rule of thumb:

Rate of enhanced oil 

production at producer

Rule of thumb:

Ratio of producers to injectors

Rule of thumb:

CO2 recycle ratio

Number of 10/11 well modules

Input:

CO2 mass 

flow rate

Compressor size and power requirement

Figure 5-2

EOR cost model overview diagram
Enhanced Oil Recovery

5-9

Table 5-2

Estimated CO2 effectiveness for selected CO2-EOR projects5,6

Operator Field Basin/Region Est. Ultimate 

EOR 

(million bbl)

Est. Ultimate CO2

Sequestered 

(giga scm)

Est. CO2

Effectiveness 

(scm/bbl)

Altura Wasson 

(Denver)

Permian 348 47 136

Devon Energy SACROC Permian 169 26 153

Texaco Vacuum Permian 33 3 94

Spirit Energy Dollarhide 

(Devonian)

Permian 28 5 177

Chevron Rangely 

Weber Sand

Rocky 

Mountain

136 17 127

Merit Energy Lost Soldier 

(Tensleep)

Rocky 

Mountain

24 3 117

Anadarko Northeast 

Purdy

Mid-continent 17 2 117

Henry 

Petroleum

Sho-Vel-Tum

Mid-continent 10 3 292

PanCanadian Weyburn Saskatchewan 130 9 70

The CO2 effectiveness has been calculated for the above CO2-EOR projects by dividing the 

estimated total amount of CO2 to be sequestered, taken as being equal to 90 percent of the CO2

purchased,5 by the estimated total amount of enhanced oil to be recovered over the lifetime of the 

project. The resulting estimates of CO2 effectiveness are all, except for those for the Sho-VelTum and Weyburn Field CO2 floods, within the selected range of 85 to 227 scm (3,000 to 8,000 

scf) of CO2 per bbl of enhanced oil. In the case of the Sho-Vel-Tum flood, the use of the lessefficient immiscible displacement process to recover enhanced oil is the likely cause of the 

exceedingly high CO2 effectiveness value.1

5.4.2 Rate of Enhanced Oil Production at Producer

The average amount of enhanced oil produced per day per well over the 20-year life of the field 

is taken to be 40 bbl. Unfortunately, there is no industry ‘rule of thumb’ for the amount of 

enhanced oil production that should be allowed at each production well on a daily basis. This is 

primarily because, as explained below, such a value is not used in practice as a basis for 

determining the number of production wells required. Given this, a value equal to the average 

amount of enhanced oil produced per day per well at the Weyburn Field has been adopted. 

Based on the calculated values of average daily enhanced oil production per well for the six 

largest CO2-EOR projects, given in Table 5-3, this assumed base-case value of 40 bbl would 

seem adequate and a sensitivity range of 20 to 70 bbl appropriate.
Enhanced Oil Recovery

5-10

There is no evidence to suggest that the amount of enhanced oil produced per day per well is 

dependent on the basin in which the CO2 flood is located. The values of average daily enhanced 

oil production per well for the six largest CO2-EOR projects, all but one of which are located in 

the Permian basin, can therefore be considered typical. The fact that the average of these 

enhanced oil production per well values is 44 bbl, which is very close to the assumed base-case 

value of 40 bbl, is reassuring.

Table 5-3

Average enhanced oil production per day per well for six largest CO2-EOR projects1

Operator Field Basin/Region Production 

Wells

EOR 

Production 

(bbl/day)

EOR 

Production 

(bbl/day/well)

Altura Wasson 

(Denver)

Permian 735 29,000 40

Amerada Hess Seminole 

(Main)

Permian 408 25,900 64

Chevron Rangely Weber 

Sand

Rocky 

Mountain

341 11,208 33

ExxonMobil Salt Creek Permian 137 9,300 68

Devon Energy SACROC Permian 325 9,000 28

Altura Wasson (ODC) Permian 293 9,000 31

It should be noted that the EOR industry determines the number of production wells based, not 

on an optimal level of enhanced oil production per day per well, but rather, on a required well 

spacing. This required spacing of wells, set by a state’s gas and oil commission, can vary 

significantly. The required spacing in one state might be one well per 0.08 km2 (20 acres), while 

in another it might be one well per 1.30 km2 (320 acres). It has not been possible here to 

calculate the well numbers using this method, as doing so would require that the typical amount 

of enhanced oil produced per acre be known.14

5.4.3 Ratio of Producers to Injectors

A ratio of producers to injectors of 1 to 1.1 is used in the EOR concept design. Since this ratio 

depends largely on the injection strategy used, it is important to note here that use of WAG 

injection is assumed. For WAG injection, the ‘rule of thumb’ is that there be a rough balance 

between producers and injectors. The specific choice of a 1 to 1.1 ratio can be attributed to the 

fact that modules comprising 10 production and 11 injection wells are used as the basis for 

costing in the EIA’s ‘Costs and Indices for Domestic Oil and Gas Field Equipment and 

Production Operations’ report and this report is used here for cost data.13,14

Table 5-4 gives the ratio of producers to injectors for each of the six largest CO2-EOR projects. 

It can be seen that, for those projects using the WAG process, the number of production and 

injection wells is roughly equal. However, this is not the case for the Seminole (Main) and 

SACROC projects for which CO2 is injected continuously. The Lost Soldier (Tensleep) CO2-

EOR project, the second largest in the Rocky Mountain region, is also included in the table as it 

provides an example of a CO2 flood having a producer to injector ratio of 1 to 1.1.
Enhanced Oil Recovery

5-11

Table 5-4

Ratio of producers to injectors for selected CO2-EOR projects5,6

Operator Field Basin/Region Injection 

Strategy

Production 

Wells

Injection 

Wells

Producers:

Injectors 

Altura Wasson 

(Denver)

Permian WAG 735 385 1.9:1

Amerada 

Hess

Seminole 

(Main)

Permian Continuous 408 160 2.6:1

Chevron Rangely 

Weber Sand

Rocky 

Mountain

WAG 341 209 1.6:1

ExxonMobil Salt Creek Permian WAG 137 100 1.4:1

Devon 

Energy

SACROC Permian Continuous 325 57 5.7:1

Altura Wasson 

(ODC)

Permian WAG 293 290 1:1

Merit Energy Lost Soldier 

(Tensleep)

Rocky 

Mountain

WAG 54 60 1:1.1

5.4.4 CO2 Recycle Ratio

The CO2 recycle ratio is taken to have an average value of 1.1.15 This value is used to calculate 

the power requirements of the CO2 recycle plant. The CO2 recycle ratio increases over the 

lifetime of the CO2 flood, from effectively zero to its maximum value, as the amount of CO2

produced with the oil at the production wells increases while the amount of oil produced 

decreases. This increase in the recycle ratio is well illustrated in the case of the Rangely Weber 

Sand CO2-EOR project. During the first 10 years of CO2 flooding, 9 giga scm of net CO2

purchases and 10 giga scm of recycled CO2 were injected, giving an average recycle ratio of 1.1. 

In contrast, the recycle ratio in 1998 was close to 2.8, with an average of 1.2 million scm per day 

of net CO2 purchases and 3.3 million scm per day of recycled CO2 being injected.5

5.4.5 Reworking of Existing Wells

It is assumed that only the reworking of existing wells, as opposed to the drilling of new wells, is 

required. The maturity of the field and the choice of injection strategy together determine 

whether or not extra wells are needed. For the purpose of the EOR-concept design, the 

assumptions are made that the field has undergone primary and secondary flooding and that the 

CO2 flood uses WAG injection. A field that has been subject to secondary flooding, 

i.e., waterflooding, has both production and injection wells. For WAG injection, it is adequate to 

assume that no additional injection wells are required. While the concept design as such requires 

that no extra wells be drilled, it is important to note that the existing production and injection 

wells and production surface facilities need to be reworked for the changed reservoir conditions. 

Also, it is necessary to provide the appropriate injection surface facilities.13,14
Enhanced Oil Recovery

5-12

5.4.6 Cost Calculations

The total capital cost comprises the injection and production equipment costs, and the cost of 

refurbishing the existing wells. The O&M costs include normal daily expenses, and surface and 

subsurface maintenance costs.

The EIA ‘Costs and Indices for Domestic Oil and Gas Field Equipment and Production 

Operations’ report16 includes a scenario for secondary oil recovery using water flooding. Costs 

and indices for additional secondary oil recovery equipment and its operation are provided for a 

representative lease, located in west Texas. This lease, or module, comprises 10 production 

wells, 11 water injection wells and 1 disposal well, and the wells are nominally 4,000 feet, or 

1,219 m, deep. This scenario was modified for CO2 flooding, and used as the basis for field 

equipment and production operations costs. The capital and O&M costs on a per module basis, 

as well as the cost of power on a per kilowatt-hour basis, are given in Table 5-5.
Enhanced Oil Recovery

5-13

Table 5-5

Capital and O&M cost estimation factors

Parameter Unit Value

CAPITAL COSTS

Injection Equipment:

Recycle & Vapor Compressors $/module 1,773,000

Plant $/module 113,600

Distribution Lines $/module 77,200

Header $/module 61,100

Electrical Service $/module 97,400

Producing Equipment:

Tubing Replacement $/module 90,800

Rods & Pumps $/module 41,000

Equipment $/module 405,000

Makeover of Existing Wells $/module 605,000

O&M COSTS

Normal Daily Expenses:

Supervision & Overhead $/module 53,100

Labor $/module 62,600

Consumables $/module 7,500

Operative Supplies $/module 7,700

Pumping & Field Power $/kWh 0.044

Recycle Compressor Power $/kWh 0.044

Surface Maintenance (Repair & Services):

Labor (roustabout) $/module 32,200

Supplies & Services $/module 44,300

Equipment Usage $/module 16,300

Other $/module 2,300

Subsurface Maintenance (Repair & Services):

Workover Rig Services $/module 46,400

Remedial Services $/module 15,100

Equipment Repair $/module 11,200

Other $/module 9,900
Enhanced Oil Recovery

5-14

5.5 Design Basis

5.5.1 Module design

The EOR design is tied as closely as possible to the EIA ‘Costs and Indices for Domestic Oil and 

Gas Field Equipment and Production Operations’ report16 in order to make use of the cost data. 

This report is a continuation of the EIA series on equipment and operating costs, and cost indices 

for oil and gas leases. In addition to cost comparisons within the petroleum industry, the 

reported data are often used to assess the economic effects of specific plans and policies relating 

to the industry. Standardization of this data has occurred over the past 23 years.

The costs and cost indices provided in this report are for representative 10-well lease operations, 

with equipment and operating procedures designed by EIA staff engineers. As previously 

mentioned, each EOR lease has 10 producing wells, 11 injection wells, and 1 disposal well, and 

the wells are nominally 4,000 feet, or 1,219 m, deep. The design criteria have taken into account 

the predominant methods of operation in each region. Individual items of equipment have been 

priced by using price lists and by communication with the manufacturer or supplier of the item in 

each region. Freight and installation costs have been determined based on regional rates. All 

costs presented in the report are current to their year and are not adjusted for inflation.

The base case design is based on a CO2 effectiveness factor of 170 scm (6,000 scf) per bbl of 

enhanced oil and an enhanced oil production rate of 40 bbl per day per well. From the design 

flow rate of 3.76 million scm (7,389 tonnes) of CO2 per day, the total enhanced oil production is 

calculated using the CO2 effectiveness factor to be 22,142 bbl. Dividing this total enhanced oil 

production by the enhanced oil production rate per well, the required number of production wells 

is found to be 554. Given a producer to injector ratio of 1 to 1.1, 609 injection wells are 

required. In keeping the design consistent with the EIA modular approach, the EOR field for the 

base case therefore consists of 56 10/11 well modules. Finally, the quantities of new CO2 and 

CO2 to be recycled, assuming a maximum recycle ratio of 3, per module are 68,000 and 204,000 

scm per day, respectively. Table 5-6 summarizes the base case for EOR.
Enhanced Oil Recovery

5-15

Table 5-6

Design basis for the EOR base case

Parameter Unit EOR Base Case

CO2 Effectiveness scf/bbl enhanced oil 6,000

scm/bbl enhanced oil 170

Oil Production per Well bbl enhanced oil/day/well 40

Total Oil Production* bbl enhanced oil/day 22,142

Number of Production Wells* 554

Number of Injection Wells* 609

Number of 10/11 Well Modules* 56

New CO2* scm/d/module 68,000

Maximum Recycled CO2* scm/d/module 204,000

Well Depth m 1,219

* calculated

A key component of the EOR field is the recycle compressor as it requires a large amount of 

energy, and capital investment. The compressor use is initially minimal but, after 20 years of 

operation, it is assumed that the ratio of CO2 produced with the enhanced oil production to the 

new CO2 will reach the maximum value of 3.

The compressor is sized to handle all of the CO2 that is recycled in the 10/11 well module. The 

actual compressor is a Superior Model WG74, sized by Cooper Energy Services and priced by 

Gas Packagers, Inc. To meet the CO2 recycle requirements of the 10/11 well module, two 

compressors, each delivering 71 scm per minute are required. The base cost for each compressor 

was adjusted by Parsons to include shipping, foundations, installation and a cooling system. 

Table 5-7 gives the recycle compressor’s parameters and cost.
Enhanced Oil Recovery

5-16

Table 5-7

Recycle compressor’s parameters and cost

Parameter Unit Individual 

Compressor

Total for 10/11 

Module

Maximum CO2 to be Compressed scm/d 102,000 204,000

Compressor Type Reciprocating Reciprocating

Suction Pressure bar 7 7

psia 100 100

Discharge Pressure bar 103 103

psia 1,500 1,500

Compressor Displacement scm/min 71 142

Overall Compression Ratio 14.7 14.7

Number of Stages 2 2

Horsepower 4.2 4.2

Connected Horsepower 500 1,000

Maximum Power Consumption kW 319 638

Average Power Consumption kW 120 240

Power Consumption kW/thousand 

scm/d

3.1 3.1

Capital Cost per Compressor $/compressor 737,000 1,473,000

Total Compressor Cost $ 82,500,000

A summary of the lease equipment required for the EOR design is given in Table 5-8.
Enhanced Oil Recovery

5-17

Table 5-8

Lease equipment

Equipment 

Description

Specification Quantity

Tubing 2.375 inch, Grade J-55 40,000 ft

Sucker Rod API Class K 40,000 ft

Pump Rod API Type RWBC 10

Pumping Unit API Size M160D 173-74, 12 hp 10

Oil Flowline 2.375 inch, PVC 16,000 ft

Manifold 10 valves, 2 inch 3-way 1

Production Separator Vertical, 30 inch x 10 feet, 2,700 

barrels per day of fluid, 5.7 million 

scf/day gas

1

Vapor Compressor 500 scf/min, 0-100 psig, 115 hp 1

Test Separator 1.0 bbl/d 1

Oil Storage Tank 2,000 bbls 2

Water Disposal Pump Quintuplex, 1,000 psi, 20 hp 1

Water Disposal Line 2.375 in, 2,500 psi yield 2,000 ft

LACT Unit 2,000 bbl/d 1

1 in = 0.0254m, 1 hp = 746 J/s, 1 ft = 0.305 m, 1 cf = 0.028 cm, 1 psig = 0.069 bar, 

5.5.2 Capital and O&M Cost Inputs

All of the capital and O&M costs, except for the power costs, are found by multiplying the per 

module costs, given in Table 5-5, by the required number of modules, as detailed in Section 

5.5.1. In the case of the pumping and field, and recycle compressor, power costs, the costs per 

kilowatt-hour are multiplied by 8,760, the total hours of operation per year, and the respective 

power requirement. Table 5-10 summarizes the model inputs for the capital and O&M costs for 

the base case EOR design.
Enhanced Oil Recovery

5-18

Table 5-9

Capital and O&M cost inputs for the EOR base case

Parameter Input

Number of Modules 56

CAPITAL COSTS

Injection Equipment:

Recycle & Vapor Compressors $99,300,000

Injection Plant Confines $6,360,000

Distribution Lines $4,320,000

Header $3,420,000

Electrical Service $5,450,000

Makeover of Existing Injection Wells $33,900,000

Producing Equipment:

Tubing Replacement $5,080,000

Rods & Pumps $2,300,000

Equipment $22,700,000

Subtotal $182,800,000

O&M COSTS

Normal Daily Expenses:

Supervision & Overhead $2,970,000

Labor $3,510,000

Consumables $420,000

Operative Supplies $431,000

Pumping & Field Power (7,196 kW) $2,770,000

Recycle Compressor Power (17,946 kW) $6,910,000

Surface Maintenance (Repair & Services):

Labor (roustabout) $1,800,000

Supplies & Services $2,480,000

Equipment Usage $913,000

Other $129,000

Subsurface Maintenance (Repair & Services):

Workover Rig Services $2,600,000

Remedial Services $846,000

Equipment Repair $627,000

Other $554,000

Subtotal $27,000,000
Enhanced Oil Recovery

5-19

5.6 Model Results

This section presents costs for CO2 capture and storage for EOR for the base case, described in 

Section 5.5.1. The storage costs comprise transaction, transportation (from Chapter 4), injection 

and monitoring costs. These costs are combined with capture costs from the IGCC power plant 

referred to earlier. The results, which include the revenue generated from the sale of the 

enhanced oil produced, are given on several bases as described in Chapter 2. Appendix B 

provides a detailed description of these spreadsheets.

The Process Input sheet for the base case is presented in Table 5-10.

Table 5-10

Process input sheet for base case EOR

Design Basis Economic

IGCC Plant IGCC Plant (Case 3a)

Capacity Factor 80% CO2 Capture Cost ($/tonne captured) $14.55

Pipeline Distance 100 km COE Including CO2 Capture Cost ($/MWh) $55.08

Pipeline Distance 62 miles

Nominal pipe size (in) 12 CO 2

 Storage

Wellhead Oil Price ($/bbl) $15.00

CO 2 Storage Oil Royalty 12.5%

Well Depth (m) 1,219 Oil Credit ($/bbl) $13.13

Well Depth (ft) 4,000 Break Even Oil Price to Offset CO2 Storage Costs ($/bbl)

CO2 Effectiveness (scm/bbl) 170 Years 1-20 $10.6360

CO2 Effectiveness (scf/bbl) 6,000 Years 1-100, NPV Basis $10.6527

Oil Production (bbl/day/well) 40

Maximum Recycle Ratio 3.0

IGCC Plant & CO2 Storage

Previous Waterflooding After-Tax Discount Rate 6.09%

Levelized Carrying Charge Factor 15.0%

Enhanced Oil Recovery Input Variables

1 inch = 0.0254m

The Summary sheet for the base case is presented in Table 5-11. This summary assumes a 

Wellhead Oil Price of $15.00/bbl and an Oil Royalty of 12.5 percent.
Enhanced Oil Recovery

5-20

Table 5-11

Summary sheet for base case EOR

Process Description Capital & O&M Cost

Previous Waterflooding Yes CO2Stored (M tonne/yr) 2.158 Capital O&M

EOR Well Modules 56 CO2 Avoided (M tonne/yr) 1.694 Transaction $2,160,000 $0

Pileline Distance 100 km LC Avoided GHG (M tonne/yr) 2.022 Transport $26,800,000 $1,333,000

Well Depth (m) 1,219 LC Avoided GHG (M tonne/yr) 1.672 Sequestration $182,800,000 $27,000,000

Monitoring $0 $216,000

Economic Description

Oil Credit ($/bbl) $13.13 Oil Price ($/bbl) Total $211,800,000 $28,500,000

Break Even Oil Price to Offset CO2 Storage Costs ($/bbl)

Years 1-20

Years 1-100, NPV Basis

1. Levelized Annual CO2 Storage Costs (years 1-20) CO2 Captured Basis LC Avoided GHG Basis

(storage only)

CO2 Storage Cost, M $/yr 60.17 60.17

Enhanced Oil Revenues, M $/yr 84.86 84.86

Net CO2 Storage Cost, M $/yr (24.69) (24.69)

CO2 Stored or Avoided, M tonne/yr 2.16 2.02

Net Storage Cost, $/tonne CO2 (11.44) (12.21)

2. Levelized Annual CO2 Capture and Net Storage Costs (years 1-20) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

CO2 Capture Cost, M $/yr 31.39 31.39 31.39

Net CO2 Storage Cost, M $/yr (24.69) (24.69) (24.69)

Capture + Net Storage Cost, M$/yr 6.70 6.70 6.70

CO2 Stored or Avoided, M tonne/yr 2.16 1.69 1.67

Capture + Net Storage Cost, $/tonne CO2 3.11 3.96 4.01

3. CO2 Capture and Net Storage Costs, NPV Basis (years 1-100) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

NPV of CO2 Capture Costs, M$ 357.42 357.42 357.42

NPV of Net CO2 Storage Costs, M$ (280.04) (280.04) (280.04)

NPV of Capture + Net Storage Costs, M$ 77.38 77.38 77.38

NPV of CO2 Stored or Avoided, M tonne 24.57 19.29 19.04

Capture + Net Storage Cost (NPV Basis), $/tonne CO2 3.15 4.01 4.06

4. Cost of Electricity, $/MWh

Reference Plant (no capture) 43.98

Capture Costs (years 1-20) 11.10

Net Storage Costs (years 1-20) (8.73)

Storage Costs, Monitoring, etc. (years 21-100) 0.03

Total Cost of Electricity 46.39

Enhanced Oil Recovery Summary

$10.64

$10.65

Cost of CO2 Capture & Storage

$15.00

5.7 Sensitivity Analysis

The sensitivity of the CO2 storage cost for EOR is determined for six key parameters as well as 

for the case of no previous waterflooding. It can be seen in Figure 5-3 that increases in well 

depth, CO2 effectiveness, recycle ratio and pipeline distance cause an increase in the cost of 

storage, while increases in oil production rate and oil price decrease the storage cost. More 

noteworthy, the figure shows that changes in oil price have the greatest effect on storage cost, 

followed closely by changes in CO2 effectiveness. As is to be expected, the case of no previous 

waterflooding results in an upward shift in the cost.
Enhanced Oil Recovery

5-21

EOR Cost Sensitivity

Levelized Annual Net CO2 Storage Costs (LC Avoided GHG Basis)

(50.00)

(40.00)

(30.00)

(20.00)

(10.00)

0.00

10.00

20.00

-50% -30% -10% 10% 30% 50%

% Change in Variable

$/tonne CO2 eq. LC GHG avoided

Well Depth (waterflooded)

CO2 Effectiveness

Oil Production Rate

Oil Price

Recycle Ratio

Pipeline Distance

No Waterflooding

Figure 5-3

Sensitivity analysis for EOR

High and low cost cases have been chosen for EOR, and are presented together with the base 

case in Table 5-12. The price of oil at the wellhead is taken to have a low-end value of $12 per 

bbl and a ceiling price of $20 per bbl. For the high and low cost values for each of the six key 

parameters, the percentage change in the value from the base case is shown. This is done to 

illustrate the fact that the range in the values of some parameters is expected to be greater than 

for others. The table also shows that the high cost case assumes no previous waterflooding, 

while the low cost case assumes the field has been waterflooded as for the base case.

Table 5-12

EOR base, high cost and low cost cases

Parameter Units EOR

Base Case

EOR

High Cost Case

EOR

Low Cost Case

CO2 Effectiveness scm/bbl enhanced oil 170 227 +34% 85 -50%

Oil Production per Well bbl enhanced oil/d/well 40 20 -50% 70 +75%

Maximum Recycle Ratio 3 4 +33% 1 -67%

Oil Price $/bbl 15 12 -20% 20 +33%

Depth m 1,219 2,438 +100% 610 -50%

Pipeline Distance km 100 300 +200% 0 -100%

Previous Waterflooding Yes No - Yes -
Enhanced Oil Recovery

5-22

The results for the high and low cost cases as well as the base case are given in Table 5-13. The 

CO2 storage cost for EOR can be seen to be widely different for the high and low cost cases. In 

reality, a CO2-EOR project with parameter values approaching those of the high cost case would 

not be carried out.

Table 5-13

Results for EOR base, high cost and low cost cases

Parameter Units EOR 

Base Case

EOR 

High Cost Case

EOR 

Low Cost Case

Total Oil Production bbl enhanced 

oil/d

22,142 16,582 44,285

Number of 10/11 Well Modules 56 83 64

New CO2 scm/d/module 68,000 45,000 59,000

Maximum Recycled CO2 scm/d/module 204,000 182,000 59,000

Levelized Annual Net CO2

Storage Cost 

$/tonne CO2 Eq. 

LC GHG 

Avoided

(12.21) 73.84 (91.26)

5.8 Comparison to Literature

A comparison is made between the costs obtained for the EOR base-case design using the EIA 

‘Costs and Indices for Domestic Oil and Gas Field Equipment and Production Operations’ report 

and those calculated using cost estimation functions provided by Reference 15. The relevant 

cost functions are shown in Table 5-14.

Table 5-14

EOR cost estimation functions15

Item Unit Value

CAPITAL COSTS

Workover of existing injector $ 5 * depth(ft) + 35,000

Workover of existing producer $ 40,000

Provision of injection surface facilities $/well 22,000

Workover of production surface facilities $/well 10,000

CO2 recycle plant $ 457,000 * CO2 recycled (million 

scf per day)

O&M COSTS

CO2 recycle compression operating costs $/y 200 * CO2 recycled (million scf 

per day) * 365
Enhanced Oil Recovery

5-23

Using these cost functions, the capital and O&M costs of EOR for the base-case design were 

calculated. Table 5-15 shows the results of these calculations together with the previously 

determined base-case EOR costs.

Table 5-15

Comparison of EOR cost results15,16

Item Unit This Study Using Cost Functions

CAPITAL COSTS

Workover of injectors $ 33,900,000 33,495,000

Provision of injection surface facilities $ 13,190,000 13,398,000

Total cost injection equipment $ 47,090,000 46,893,000

Workover of producers $ 27,780,000 22,160,000

Workover of production surface

facilities

$ 2,300,000 5,540,000

Total cost production equipment $ 30,080,000 27,700,000

CO2 recycle plant $ 105,660,000 182,354,000

Total Capital Cost $ 182,800,000 256,947,000

O&M COSTS

CO2 recycle compression operating

costs

$/yr 12,232,000 14,540,000

TOTAL O&M COSTS $/yr 27,000,000 -

Except for the CO2 recycle plant, it can be seen from the table that the cost of the EOR base-case 

design is very similar for the two sets of cost data. The CO2 recycle plant costs were not 

included in the EIA report and were developed from vendor quotations and in-house data. It is 

concluded that using the EIA report for costs was reliable, given the uncertainties and variation 

in the data.

5.9 References

1 “OGJ Special – Worldwide EOR survey 2000,” Oil & Gas Journal, pp. 44-61, Mar. 20, 

2000.

2 “OGJ Special – Worldwide production,” Oil & Gas Journal, pp. 126-157, Dec. 24, 2001.

3 Herzog, H., “An introduction to CO2 separation and capture technologies,” Energy

Laboratory Working Paper, 1999.
Enhanced Oil Recovery

5-24

4 Kinder Morgan CO2 Company, “CO2 Supply,” [Online document], 2001, [cited Jan. 2002], 

Available HTTP: 

http://www.kne.com/co2/supply.cfm

5 “Enhanced Oil Recovery Scoping Study,” EPRI, Palo Alto, CA. Tech. Rep. 113836, 1999.

6 Brown, K., W. Jazrawi, R. Moberg and M. Wilson, “Role of enhanced oil recovery in 

carbon sequestration – The Weyburn Monitoring Project, a case study,” presented at the 

First National Conference on Carbon Sequestration, Washington DC, May 14-17, 2001.

7 Moritis, G., “Future of EOR & IOR: New companies, infrastructure, projects reshape 

landscape for CO2 EOR in U.S.,” Oil & Gas Journal, May 14, 2001.

8 Marle, C.M., “Oil entrapment and mobilization,” In Basic Concepts in Enhanced Oil 

Recovery Processes, M. Baviere, Eds. Elsevier Applied Science, 1991, pp. 3-39.

9 Klins, M.A. and C.P. Bardon, “Carbon dioxide flooding,” In Basic Concepts in Enhanced 

Oil Recovery Processes, M. Baviere, Eds. Elsevier Applied Science, 1991, pp. 215-240.

10 Morel, D., “Miscible gas flooding,” In Basic Concepts in Enhanced Oil Recovery Processes, 

M. Baviere, Eds. Elsevier Applied Science, 1991, pp. 185-214.

11 "Evaluation of Innovative Fossil Fuel Power Plants with CO2 Removal," EPRI Report No. 

1000316, Interim Report, December 2000, Cosponsors: U.S. Department of Energy/Office 

of Fossil Energy and U.S. Department of Energy/NETL, EPRI Project Manager N. A. H. 

Holt. 

Available HTTP:

http://www.netl.doe.gov/coalpower/gasification/pubs/pdf/EpriReport.pdf

12 Smith, L.A. et al, “Engineering and economic assessment of carbon dioxide sequestration in 

saline formations,” presented at the First National Conference on Carbon Sequestration, 

Washington DC, May 14-17, 2001.

13 Wilson, Malcolm, personal communication, Sep. 24, 2001.

14 Hattenbach, Ray, personal communication, Sep. 24, 2001.

15 “Is my field a candidate for CO2 flooding?,” Center for Energy and Economic 

Diversification (CEED), Odessa, TX. CEED CO2 flooding shortcourse no. 2, 1995.

16 Energy Information Administration – Office of Oil and Gas, “Costs and Indices for

Domestic Oil and Gas Field Equipment and Production Operations,” [Online document], 

Mar. 2000, Available HTTP:

http://www.eia.doe.gov/oil_gas/natural_gas/data_publications/cost_indices/c_i.html
6-1

6

ENHANCED COALBED METHANE

6.1 Introduction

This chapter looks at the injection of CO2 into deep coal seams as a means of enhancing coalbed 

methane production while simultaneously sequestering CO2.

6.2 State of the Art

6.2.1 Applications

The injection of CO2 into deep, unmineable coal seams to enhance coal-bed methane production 

(CO2-ECBMR) is a relatively recent technology. It was not until 1996 that the world’s first, and 

to date only, pilot-scale application of CO2-ECBMR began operation. In contrast, EOR using 

CO2 floods (CO2-EOR) is a mature technology with over three decades of commercial-scale 

application. The one CO2-ECBMR project, comprising nine coal-bed methane (CBM) 

production and four CO2 injection wells, is located in the southwestern United States within the 

Allison production unit of the San Juan basin and is operated by Burlington Resources, the 

United States’ largest producer of coal-bed methane. Analysis of operations at the Allison unit 

has shown the CO2-ECBMR process to be technically and economically feasible.1,2,3 

6.2.2 Storage Potential

CBM production has become an increasingly important component of natural gas supply in the 

United States during the last decade. In 2000, approximately 40 billion standard cubic meters 

(scm) of CBM was produced, accounting for about 7 percent of the nation’s total natural gas 

production. The most significant CBM production, some 85 percent of the total, occurs in the 

San Juan basin of southern Colorado and northern New Mexico. Another 10 percent is produced 

in the Black Warrior basin of Alabama and the remaining 5 percent comes from rapidly 

developing Rocky Mountain coal basins, namely the Uinta basin in Utah, the Raton basin in 

Colorado and New Mexico, and the Powder River basin in Wyoming.1,4

Essentially all current CBM production utilizes primary recovery methods. Primary recovery 

involves pumping off large volumes of formation water to lower reservoir pressure and cause 

methane desorption from the coal. Primary production of CBM recovers only 20 to 60 percent 

of original gas-in-place (OGIP), where this varies depending on reservoir properties such as coal 

seam permeability and gas saturation, and operational practices such as well spacing. In 

comparison, over 90 percent of the OGIP can theoretically be recovered using CO2-ECBMR.
Enhanced Coalbed Methane

6-2

Furthermore, CO2-ECBMR can accelerate CBM recovery, providing greater real value for a 

given reserve.1,2

Significant potential for CO2-ECBMR exists worldwide. In order for CO2-ECBMR to be 

successfully applied, reservoirs must have laterally continuous and permeable coal seams, 

concentrated seam geometry, and minimal faulting and reservoir compartmentalization. In the 

United States, the geologically most favorable reservoirs are located within the San Juan, Uinta, 

and Raton basins, while additional potential exists in the Greater Green River and Appalachian 

basins. A number of coal basins in Australia, Russia, China, India, Indonesia, and other 

countries have also been identified as having large CO2-ECBMR potential. Indeed, the total 

worldwide potential for CO2-ECBMR, taking into consideration only those reservoirs where 

CO2-ECBMR could be profitably developed without CO2 sequestration credits or free or 

reduced-cost CO2 supplies, is estimated at around two trillion scm of CBM, with about 

7.1 billion tonnes of associated CO2 sequestration potential.5

6.2.3 Storage Mechanics

Four patents have been issued over the past two decades relating to the CO2-ECBMR process. 

Each of these patents is based on the principle that CO2 is adsorbed more readily onto the coal 

matrix than methane. Specifically, CO2-ECBMR involves injected CO2 being adsorbed at the 

expense of methane, which having been displaced can be recovered as a free gas at production 

wells. Sorption isotherm measurements in the laboratory indicate that two unit volumes of CO2

are required to displace one unit volume of methane. This ratio of CO2 effectiveness is however 

expected to vary in the field according to the thermal maturity of the coal.1,2,5,6

A successful demonstration of CO2-ECBMR technology has been provided by the Allison unit 

pilot project. Prior to CO2 injection, CBM was produced within the unit using conventional 

pressure-depletion methods. Since the start of CO2 injection, enhanced CBM production has 

been observed. A marked increase in water production was also observed initially, signaling 

improved sweep of bypassed reservoir areas that should lead to higher ultimate gas recovery. 

Finally, there has been negligible CO2 breakthrough, despite around one billion scf of CO2 being 

injected each year since the project began in 1996. The injected CO2 comes from the McElmo 

Dome, which is a natural CO2 deposit in southwestern Colorado.1,2,3

6.2.4 Feasibility of Storage Option

CO2-ECBMR presents an attractive option for the sequestering of CO2. Like CO2-EOR, it has 

the distinct advantage over other CO2 storage options that it sequesters CO2 while also 

generating a value-added product. While CO2-ECBMR as a technology is still in the 

development stage and has not been widely applied, it has been successfully demonstrated in a 

pilot-scale application. Also, given the broad similarities between the two, the technology 

required to implement CO2-ECBMR in the field can be largely based on that used for CO2-EOR 

operations. The fact that CO2 is adsorbed onto the coal surface means that there should be little 

risk of leakage of CO2 from the reservoir. Also on a positive note, coal, and so CBM, typically 
Enhanced Coalbed Methane

6-3

lies at relatively shallow depths so that well drilling and completion costs are generally lower 

than for other geologic options.

On the downside, CO2-ECBMR is a very energy intensive process, requiring significant 

electricity both for pumping large volumes of formation water to the surface and for compressing 

the produced methane to a suitable pressure for pipeline transport and sale. Another 

disadvantage is that the large volumes of formation water produced by CO2-ECBMR are most 

often saline and need to be disposed of in an environmentally acceptable manner.

6.3 Process Description

Figure 6-1 is a block flow diagram, indicating the overall flow and distribution of CO2 from the 

IGCC plant to the ECBMR field. First, the CO2 leaving the plant is fed to an additional stage of 

compression to bring it up to the required pipeline inlet pressure. Second, the pipeline transports 

the CO2 a distance of 100 km to the ECBMR field, where it is injected into the ECBMR CO2

wells. Third, the ECBMR product is dewatered, and dry gas from the ECBMR wells is 

compressed to the gathering line pressure. Finally, the gas from the gathering line is then further 

compressed for sale to a nearby pipeline.

The source and quantity of CO2 supplied to the ECBMR field is the same as for the EOR storage 

option. It should also be noted that the pipeline outlet pressure of 103 bar is assumed to be at or 

above the required surface injection pressure. 

Very simple vertical wells from 300 to 1,200 m in total depth are common to this type of 

production. These wells produce gas at very low pressures; wellhead pressures of between 2 to 

3 bar are common. 7 Because these wells are generally operated at low backpressures (assuming 

1.7 bar), compression is required to increase the wellhead pressure to the gas gathering line 

pressure of 4.5 bar. The gathered gas is then further compressed to 25.1 bar for delivery to a 

nearby pipeline.

Developing an ECBMR lease for production involves:

• Lease acquisition activities

• Drilling and equipping production/injection wells

• Installation of high-pressure injection equipment and related piping

• Installation of ECBMR production equipment and facilities

• Installation of product gas compressors

The ECBMR field is a grass-roots facility, which has not produced CBM in the past. The 

ECBMR field therefore requires a new distribution and injection/ECBMR production system, 

which serves the following purposes:

• Receives CO2 from the pipeline terminal and distributes it to the ECBMR CO2 injection 

wells.
Enhanced Coalbed Methane

6-4

• Gathers gas from the ECBMR production wells and delivers it to a central gas/liquid 

separator.

• Dewaters the ECBMR production wells and conveys water to a central disposal well.

• Compresses the separated gas to 4.5 bar for distribution to a regional gathering line.

• Compresses the gathered gas to 25.1 bar for sale to a nearby pipeline. 

Most coal-bed methane reservoirs are low-pressure water-bearing gas reservoirs. Under 

conditions of high water saturation, the water volume and the hydrostatic pressure must be 

reduced by artificial lift to initiate gas desorption and flow to the wellbore.8 This dewatering 

process produces large quantities of saline water that must be disposed of carefully. In the 

Warrior Basin, water is usually piped to a central treatment facility and disposed into a surface 

stream. In the San Juan Basin, because of the higher total dissolved solids in the water, disposal 

wells are used.9 For this study, the use of disposal wells is assumed.

A productive life span of 20 to 30 years is typical for coal-bed methane fields.10 The life of this 

field is assumed to be the same as that of the power plant, 20 years. As a final note, the 

design/construction time is taken to be the same as the power plant, 4 years.
Enhanced Coalbed Methane

6-5

10 Producing Wells

90 kW

10 CO2Injection Wells

20 kW

Inlet Separator

Free Water 

Separator

Gathering

Compressor

4.5 bar

157 kW

CO2

Water

Water Injection 

Well

10/10 Well

ECBM Module

Total

CO2 Flow

CO2 Pipeline

Terminal

152 bar

Boost

Compressor

2,650 kW

100 km

Pipeline,

12” diameter

7,389

tonne /day

83 bar

IGCC Plant

CO2 Source

Block Flow Diagram:

ECBMR

547 tonne/day

Methane

Water

140 Mscmd 2.4 bar

1.88 MMscmd Gathering Line

Sales Gas

25 bar

5,655 kW

Figure 6-1

ECBMR block flow diagram
Enhanced Coalbed Methane

6-6

6.4 Methodology Used

As in the case of the CO2-EOR concept design, ‘rules of thumb’ are used to define the 

engineering parameters needed to estimate the cost of a CO2-ECBMR project. As for CO2-EOR, 

the method for costing the CO2-ECBMR process is also split up into a number of steps. First, the 

total amount of enhanced CBM produced per day for the given CO2 mass flow rate is determined 

using a CO2 effectiveness factor of two scm CO2 per scm of enhanced CBM. Second, the 

number of production wells is found by dividing this total amount of enhanced CBM produced 

per day by an assumed 14,000 scm of enhanced CBM per day being produced at each well. 

Third, a ratio of producers to injectors of 1 to 1 is used to calculate the number of injection wells 

from the number of production wells. Fourth, it is assumed that no recycling of CO2 is required. 

Finally, the cost of drilling and equipping the required production and injection wells is 

calculated. An overview of the cost model is provided in Figure 6-2.

6.4.1 CO2 Effectiveness

For the design basis, it is assumed that 2 scm of CO2 needs to be injected to produce 1 scm of 

enhanced CBM. This CO2 effectiveness ratio is based on the results of sorption isotherm 

measurements carried out on bituminous coals in the laboratory. These measurements indicate 

that coal can adsorb roughly twice as much CO2 by volume as methane. This may vary, 

however, as a result of other physical processes active within a coal reservoir. Based on data 

collected in the field, one source11 has reported the amount of CO2 injected to CBM produced as 

being between 1.5 and 2 while another12 has reported it as being closer to 3 than 2. It is 

important to note that the ratio is also dependent on the thermal maturity of the coal and that it 

can be as high as 10 to 1 for sub-bituminous coals. Based on these values, a range of 1.5 to 

10 scm of CO2 per scm of enhanced CBM was chosen for the sensitivity ana lysis.1,2,5

6.4.2 Rate of Enhanced CBM Production

The amount of enhanced CBM produced per day at each well is taken to be 14,000 scm. As in 

the case of EOR, there is no industry ‘rule of thumb’ for the production at each well on a daily 

basis. Instead, the CBM production rate depends on reservoir parameters such as coal seam 

permeability, gas saturation and thickness, and operational practices such as the recovery method 

used and well spacing.13

The variation in the CBM production rate that results from different values of reservoir 

parameters can be seen from a comparison of values for the San Juan and Black Warrior basins. 

Average production in the San Juan basin exceeds 23,000 scm per day per well, with many wells 

in the most productive area averaging over 85,000 scm per day. In contrast, the Black Warrior 

basin wells average 3,400 scm per day, reflecting the fact that this basin has lower permeability, 

thinner coal seams.
Enhanced Coalbed Methane

6-7

COST MODEL

Internal Calcs:

Capital costs:

Front end, lease acquisition, injection 

equipment, producing equipment, production & 

injection wells, gathering system 

O&M costs:

Normal daily expenses, surface maintenance, 

Inputs:

Well depth

Capital charge rate

Outputs:

Total capital cost

Total O&M cost

Total annual cost 

Total cost per tonne CO2

Input:

CO2 mass 

flow rate

ENHANCED CBM 

PRODUCTION 

CALCULATION

Total enhanced CBM production

PRODUCTION 

WELL NUMBER 

CALCULATION

INJECTION 

WELL NUMBER 

CALCULATION

Number of production wells

Rule of thumb:

CO2 effectiveness

Rule of thumb:

Rate of enhanced CBM 

production at producer

Rule of thumb:

Ratio of producers to injectors

Number of 10/10 well modules

Figure 6-2

ECBMR cost model overview diagram
Enhanced CoalBed Methane

6-8

The effect of the recovery method on the CBM production rate is evident from a look at the 

production history of the #115 well within the Allison unit of the San Juan basin. Prior to CO2

injection, the #115 well had been a sub-average performer, with a CBM production rate of 

14,000 scm per day. However, following CO2 injection, the daily CBM production rate rose 

sharply to 37,000 scm.2,13

Based on the values given above for projects without CO2 injection as well as the Allison unit 

CO2-ECBMR pilot project, and advice from experts in the field11,12, the assumed base-case value 

of 14,000 scm of enhanced CBM per day per well was chosen. A sensitivity analysis range of 

3,000 to 30,000 scm of enhanced CBM per day per well was also chosen. It is important to note 

that the values for the base-case and sensitivity range were chosen to be somewhat lower than 

those values quoted for the San Juan basin. This is simply because the San Juan basin, being the 

world’s most prolific basin for CBM, is expected to have higher CBM production rates than 

other coal basins with CO2-ECBMR potential.

6.4.3 Ratio of Producers to Injectors

A ratio of producers to injectors of 1 to 1 is assumed for the CO2-ECBMR concept design. It is 

an industry standard for production and injection wells to be arranged in a five-spot 

configuration, where this entails each injector being surrounded by four producers. This well 

configuration is used in the case of the Allison unit CO2-ECBMR pilot project, which comprises 

nine CBM production and four CO2 injection wells. The ratio of producers to injectors resulting 

from the five-spot configuration for this small number of wells is just over 2 to 1. However, as 

the number of production and injection wells increases, a repeating five-spot configuration 

results and the ratio of producers to injectors steadily approaches 1 to 1. For the CO2-ECBMR 

concept design, which comprises a relatively large number of wells, a ratio of producers to 

injectors of 1 to 1 is therefore used.

6.4.4 CO2 Recycle Ratio

The CO2-ECBMR concept design assumes that CO2 breakthrough at the production wells is 

negligible and that there is, therefore, no need for CO2 recycling. At the Allison unit, 

breakthrough of CO2 has been minimal during the life of the project. Following almost five 

years of injection, the CO2 concentration in the produced gas was about 0.6 percent, which is 

only slightly above pre-injection levels of 0.4 percent.3

6.4.5 Drilling and Equipping of Production and Injection Wells

The cost of the CO2-ECBMR process is calculated based on both production and injection wells 

needing to be drilled and equipped. If a coal bed is viewed primarily as a source of CBM, it 

makes more economic sense to partially deplete the reservoir of CBM before injecting CO2. 

However, in the case that the primary role of the coal bed is as a repository for CO2, early use of 

CO2-ECBMR is favored. Given that the concern here is CO2 sequestration, it is assumed for the 
Enhanced Coalbed Methane

6-9

purpose of the concept design that no CBM production has taken place at the coal bed prior to 

CO2 injection. This assumption implies that production and injection wells need to be provided.5

6.4.6 Cost Calculations

The total capital cost comprises front end and lease acquisition, injection and production 

equipment, well drilling and gathering system costs. The O&M costs include normal daily 

expenses, and surface and subsurface maintenance costs.

Prior to acquiring a lease position, geological expenditures, geophysical expenditures, and 

engineering-based feasibility studies are often conducted. In addition, outlays are generally 

required for obtaining the lease and its associated permits. These front-end transaction costs will 

vary greatly but may range from $20,000 to $30,000 per well for a commercial project.9 For this 

study, a cost of $25,000 per well is assumed.

All of the other field costs, except for the well drilling cost, are based on data contained in the 

EIA ‘Costs and Indices for Domestic Oil and Gas Field Equipment and Production Operations’ 

report.14 A representative ECBMR lease, or module, comprising 10 CO2 injection wells and 

10 producing wells with dewatering facilities is used for the design basis. The 10 CO2 injection 

wells are drilled to a depth of 610 m and equipped with a battery of lease equipment, which 

includes distribution lines, headers, electrical service, and controls. The 10 producing wells, also 

drilled to a depth of 610 m, are equipped with beam balanced/sucker rod dewatering.

The well drilling cost is calculated based on a relationship derived from data contained in the 

‘1998 Joint Association Survey (JAS) on Drilling Costs’ report.15 This relationship between well 

depth and drilling cost is shown in Figure 6-3.

Well Drilling Cost as a Function of Depth

1998 Onshore Gas & Oil Well Data

y = 0.0888e0.0008x

0.10

1.00

10.00

0 1000 2000 3000 4000 5000 6000

Depth (m)

Cost ($M)

Figure 6-3

Well drilling cost as a function of depth
Enhanced CoalBed Methane

6-10

The field equipment and well drilling capital costs, and the associated O&M costs, are shown on 

a per module basis in Table 6-1. This table also gives the power costs associated with the 

gathering and sales gas compressors on a per kilowatt-hour basis.

Table 6-1

Capital and O&M cost estimation factors

Parameter Unit Value

CAPITAL COSTS

Injection Equipment:

Plant $/module 104,455

Distribution Lines $/module 70,182

Header $/module 55,545

Electrical Service $/module 87,818

Producing Equipment:

Tubing $/module 40,800

Rods & Pumps $/module 39,200

Pumping Equipment $/module 340,000

Gathering System:

Flowlines $/module 42,500

Manifold $/module 42,600

Gathering Compressor $/module 105,000

Sales Gas Compressor $/module 3,970,000

Lease Equipment:

Producing Separator $/module 12,400

Storage Tanks $/module 76,600

Accessory Equipment $/module 35,800

Disposal System $/module 96,700

Production & Injection Wells $/module 1,446,601

O&M COSTS

Normal Daily Expenses:

Supervision & Overhead $/module 50,245

Labor $/module 39,936

Consumables $/module 7,664

Operative Supplies $/module 4,518

Auto Usage $/module 7,900

Pumping & Field Power $/kWh 0.044

Gathering Compressor $/kWh 0.044
Enhanced Coalbed Methane

6-11

Table 6-1 (continued)

Capital and O&M cost estimation factors

Parameter Unit Value

Sales Gas Compressor $/kWh 0.044

Surface Maintenance (Repair & Services):

Labor (roustabout) $/module 18,282

Supplies & Services $/module 27,182

Equipment Usage $/module 7,064

Other $/module 2,782

Subsurface Maintenance (Repair & Services):

Workover Rig Services $/module 30,518

Remedial Services $/module 8,145

Equipment Repair $/module 7,400

Other $/module 6,764

6.5 Design Basis

6.5.1 Module Design

The ECBMR design is tied as closely as possible to the EIA ‘Costs and Indices for Domestic Oil 

and Gas Field Equipment and Production Operations’ report14 in order to make use of the cost 

data. This report is described in detail in Section 5.5.1.

The base case design is based on a CO2 effectiveness factor of 2 scm per scm of enhanced CBM 

and an enhanced CBM production rate of 14,000 scm per day per well. From the design flow 

rate of 3.76 million scm (7,389 tonnes) of CO2 per day, the total enhanced CBM production is 

calculated using the CO2 effectiveness factor to be 1.88 million scm. Dividing this total 

enhanced CBM production by the enhanced CBM production rate per well, the required number 

of production wells is found to be 135. Given a producer to injector ratio of 1 to 1, 135 injection 

wells are required. In keeping the design consistent with the EIA modular approach, the 

ECBMR field for the base case therefore consists of 14 10/10 well modules. Finally, a well 

depth of 610 m, which is slightly more than the average depth of the CBM wells reported in the 

‘1998 JAS on Drilling Costs’ report, is selected as typical. Table 6-2 summarizes the base case 

for ECBMR.
Enhanced CoalBed Methane

6-12

Table 6-2

Design basis for ECBMR base case

Parameter Unit ECBMR Base Case

CO2 Effectiveness scm/scm enhanced CBM 2.0

CBM Production per Well scm enhanced CBM/d/well 14,000

Total CBM Production* million scm enhanced CBM/d 1.88

Number of CBM Wells* 135

Number of CO2 Wells* 135

New CO2* scm/d/well 28,000

Well Depth m 610

Each ECBMR field requires a gathering line compressor to transfer dewatered methane from the 

10 producing wells to a connecting pipeline. The methane from the wells is fed to a common 

pipe at 1.7 bar and compressed to 4.5 bar. Table 6-3 indicates the basis for the gathering 

compressor design and the compressor requirements.

Table 6-3

ECBMR gathering compressor design basis

Parameter Unit Value

Maximum Methane Rate thousand scm/d 140

Suction Pressure bar 2.4

psia 24.7

Discharge Pressure bar 4.5

psia 64.7

Compressor Displacement cmm 41

Compression Ratio 1.875

Compressor Configuration Motor Driven Reciprocating

Maximum Horsepower 210

Maximum Connected 

Power

kW 157

Compressor Cost $ 105,000
Enhanced Coalbed Methane

6-13

A second compressor is required for sending the gathered gas from all the modules though a 

common sales gas line to a nearby pipeline. The gas must be compressed to 25.1 bar for transfer 

to the pipeline. Table 6-4 indicates the basis for the sales gas compressor design and the 

compressor requirements.

Table 6-4

ECBMR sales gas compressor design basis

Parameter Unit Value

Maximum Methane Rate million scm/d 1.88

Suction Pressure bar 4.5

psia 64.7

Discharge Pressure bar 25.1

psia 364.7

Compressor Displacement cmm 291

Compression Ratio 5.637

Compressor Configuration Motor Driven Reciprocating

Maximum Horsepower 7,580

Maximum Connected 

Power

kW 5,655

Sales Gas Compressor 

Cost

$ 3,970,000

A summary of lease equipment required for the ECBMR design is given in Table 6-5.
Enhanced CoalBed Methane

6-14

Table 6-5

Lease equipment

Equipment 

Description

Specification Quantity

Tubing 2.375 inch, Grade J-55 20,000 ft

Sucker Rod API Class K 20,000 ft

Pump Rod API Type RWBC 10

Pumping Unit API Size M160D 173-74, 20 hp 10

Flowline 4 inch, Schedule 40 Steel 16,000 ft

Manifold 10 valves, 2 inch 3-way 1

Production Separator Vertical, 30 inch x 10 feet, 5.0 million 

scf/d gas

1

Storage Tank 50,000 gallon 2

Water Disposal Pump Quintuplex, 1,000 psi, 20 hp 1

Water Disposal Line 3 inch, Schedule 40 Steel 2,000 ft

Gas Meter million scf/d 1

1 in = 0.0254m, 1 hp = 746 J/s, 1 ft = 0.305 m, 1 cf = 0.028 cm, 1 psig = 0.069 bar, 

6.5.2 Capital and O&M Cost Inputs

All of the capital and O&M costs, except for the power costs, are found by multiplying the per 

module costs, given in Table 6-1, by the required number of modules, as detailed in 

Section 6.5.1. In the case of the gathering compressor and sales gas compressor power costs, the 

costs per kilowatt-hour are multiplied by 8,760, the total hours of operation per year, and the 

respective power requirement. Table 6-6 summarizes the model inputs for the capital and O&M 

costs for the base case EOR design.
Enhanced Coalbed Methane

6-15

Table 6-6

Capital and O&M cost inputs for the ECBMR base case

Parameter Input

Number of Modules

CAPITAL COSTS

Front End & Lease Acquisition Costs $6,750,000

Injection Equipment:

Plant $1,410,000

Distribution Lines $947,000

Header $750,000

Electrical Service $1,190,000

Producing Equipment:

Tubing $551,000

Rods & Pumps $529,000

Pumping Equipment $4,590,000

Gathering System:

Flowlines $574,000

Manifold $575,000

Gathering Compressor $1,420,000

Sales Gas Compressor $3,970,000

Lease Equipment:

Producing Separator $167,000

Storage Tanks $1,030,000

Accessory Equipment $483,000

Disposal System $1,310,000

Production & Injection Wells $39,100,000

Subtotal $65,300,000

O&M COSTS

Normal Daily Expenses:

Supervision & Overhead $678,000

Labor $539,000

Consumables $103,000

Operative Supplies $61,000

Auto Usage $107,000

Pumping & Field Power (1,485 kW) $572,000
Enhanced CoalBed Methane

6-16

Table 6-6 (continued)

Capital and O&M cost inputs for the ECBMR base case

Parameter Input

Gathering Compressor (2,120 kW) $817,000

Sales Gas Compressor Power (6,654 kW) $2,180,000

Surface Maintenance (Repair & Services):

Labor (roustabout) $247,000

Supplies & Services $367,000

Equipment Usage $95,400

Other $37,600

Subsurface Maintenance (Repair & Services):

Workover Rig Services $412,000

Remedial Services $110,000

Equipment Repair $100,000

Other $91,300

Subtotal $6,520,000

6.6 Model Results

This section presents costs for CO2 capture and storage for ECBMR for the base case, described 

in Section 6.5. The storage costs comprise transaction, transportation, injection and monitoring 

costs. These costs are combined with capture costs from an IGCC power plant. The results, 

which include the revenue generated from the sale of the enhanced CBM produced, are given on 

several bases as described in Chapter 2. Appendix B provides a detailed description of these 

spreadsheets.

The Process Input sheet for the base case is presented in Table 6-7.
Enhanced Coalbed Methane

6-17

Table 6-7

Process input sheet for base case ECBMR

Design Basis Economic

IGCC Plant IGCC Plant (Case 3a)

Capacity Factor 80% CO2 Capture Cost ($/tonne captured) $14.55

Pipeline Distance 100 km COE Including CO2 Capture Cost ($/MWh) $55.08

Pipeline Distance 62 miles

Nominal pipe size (in) 12 CO2

 Storage

Gas Royalty 12.5%

CO2 Storage Gas Price ($/106

 BTU) $2.00

Well Depth (m) 610 Gas Credit ($/106

 BTU) $1.75

Well Depth (ft) 2,000 Break Even Gas Price to Offset CO2 Storage Costs ($/106

 BTU)

CO2 Effectiveness (scm CO2/scm CBM) 2.0 Years 1-20 $1.3073

CBM Production (scm/day/well) 14,000 Years 1-100, NPV Basis $1.3129

IGCC Plant & CO2 Storage

After-Tax Discount Rate 6.09%

Levelized Carrying Charge Factor 15.0%

Enhanced Coal Bed Methane Input Variables

106Btu = 1.06 GJ, 1 inch = 0.0254m

The Summary sheet for the base case is presented in Table 6-8. This summary assumes a Gas 

Price of $1.90/giga joule ($2.00/million Btu) and a Gas Royalty of 12.5 percent.
Enhanced CoalBed Methane

6-18

Table 6-8

Summary sheet for base case ECBMR

Process Description Capital & O&M Cost

Pileline Distance 100 km CO2 Stored (M tonne/yr) 2.158

Well Depth (m) 610 CO2 Avoided (M tonne/yr) 1.775 Capital O&M

CBM Production (million scm/day) 1.88 LC Avoided GHG (M tonne/yr) 2.106 Transaction $2,160,000 $0

LC Avoided GHG (M tonne/yr) 1.756 Transport $26,800,000 $1,333,000

Sequestration $65,300,000 $6,520,000

Economic Description Monitoring $0 $216,000

After-Tax Discount Rate 6.09%

Gas Price ($/106 BTU) $2.00 Gas Credit ($/106 BTU) Total $94,300,000 $8,100,000

Break Even Gas Price to Offset CO2 Storage Costs ($/106 BTU)

Years 1-20

Years 1-100, NPV Basis

1. Levelized Annual CO2 Storage Costs (years 1-20) CO2 Captured Basis LC Avoided GHG Basis

(storage only)

CO2 Storage Cost, M $/yr 22.20 22.20

Gas Revenues, M $/yr 33.96 33.96

Net CO 2 Storage Cost, M $/yr (11.76) (11.76)

CO2 Stored or Avoided, M tonne/yr 2.16 2.11

Net Storage Cost, $/tonne CO2 (5.45) (5.59)

2. Levelized Annual CO2 Capture and Net Storage Costs (years 1-20) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

CO2 Capture Cost, M $/yr 31.39 31.39 31.39

Net CO 2 Storage Cost, M $/yr (11.76) (11.76) (11.76)

Capture + Net Storage Cost, M$/yr 19.63 19.63 19.63

CO2 Stored or Avoided, M tonne/yr 2.16 1.78 1.76

Capture + Net Storage Cost, $/tonne CO2 9.10 11.06 11.18

3. CO2 Capture and Net Storage Costs, NPV Basis (years 1-100) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

NPV of CO2 Capture Costs, M$ 357.42 357.42 357.42

NPV of Net CO2 Storage Costs, M$ (132.86) (132.86) (132.86)

NPV of Capture + Net Storage Costs, M$ 224.56 224.56 224.56

NPV of CO2 Stored or Avoided, M tonne 24.57 20.21 19.99

Capture + Net Storage Cost (NPV Basis), $/tonne CO2 9.14 11.11 11.23

4. Cost of Electricity, $/MWh

Reference Plant (no capture) 43.98

Capture Costs (years 1-20) 11.10

Net Storage Costs (years 1-20) (4.16)

Storage Costs, Monitoring, etc. (years 21-100) 0.03

Total Cost of Electricity 50.96

Enhanced Coal Bed Methane Summary

$1.31

$1.31

Cost of CO2 Capture & Storage

$1.75
Enhanced Coalbed Methane

6-19

6.7 Sensitivity Analysis

The sensitivity of the CO2 storage cost for ECBMR is determined for five key parameters: well 

depth, CO2 effectiveness, CBM production rate, gas price and pipeline distance. It can be seen in 

Figure 6-4 that increases in well depth, CO2 effectiveness and pipeline distance cause an increase 

in the cost of storage, while increases in CBM production rate and gas price decrease the storage 

cost. More noteworthy, the figure shows that changes in gas price have the greatest effect on 

storage cost, followed closely by changes in CO2 effectiveness.

ECBMR Cost Sensitivity

Levelized Annual Net CO2 Storage Costs (LC Avoided GHG Basis)

(18.00)

(16.00)

(14.00)

(12.00)

(10.00)

(8.00)

(6.00)

(4.00)

(2.00)

0.00

2.00

4.00

-50% -30% -10% 10% 30% 50%

% Change in Variable

$/tonne CO2 eq. LC GHG avoided

Well Depth

CO2 Effectiveness

CBM Production Rate

Gas Price

Pipeline Distance

Figure 6-4

Sensitivity analysis for ECBMR

High and low cost cases have been chosen for ECBMR, and are presented together with the base 

case in Table 6-9. The price of gas at the wellhead is taken to have a low-end value of $1.80 per 

giga joule and a ceiling price of $3 per giga joule. For the high and low cost values for each of 

the five key parameters, the percentage change in the value from the base case is shown. This is 

done to illustrate the fact that the range in the values of some parameters is expected to be greater 

than for others.
Enhanced CoalBed Methane

6-20

Table 6-9

ECBMR base, high cost and low cost cases

Parameter Units ECBMR

Base 

Case

ECBMR

High Cost Case

ECBMR

Low Cost Case

CO2

Effectiveness

scm/scm enhanced 

CBM

2 10 +400% 1.5 -33%

CBM 

Production 

per Well

scm enhanced 

CBM/d/well

14,000 3,000 -79% 30,000 +114%

Gas Price $/giga joule 2 1.80 -10% 3 +50%

Depth m 610 1,219 +100% 610 0%

Pipeline 

Distance

km 100 300 +200% 0 -100%

The results for the high and low cost cases as well as the base case are given in Table 6-10. The 

CO2 storage cost for ECBMR can be seen to be widely different for the high and low cost cases. 

In reality, a CO2-ECBMR project with parameter values approaching those of the high cost case 

would not be carried out.

Table 6-10

Results for ECBMR base, high cost and low cost cases

Parameter Units ECBMR

Base Case

ECBMR

High Cost 

Case

ECBMR 

Low Cost 

Case

Total CBM Production (million scm 

enhanced 

CBM/d)

1.88 0.38 2.51

Number of CBM Wells 135 126 84

Number of CO2 Wells 135 126 84

New CO2 scm/d/well 28,000 30,000 45,000

Levelized Annual Net 

CO2 Storage Cost 

$/tonne CO2

Eq. LC GHG 

Avoided

(5.59) 18.88 (25.72)

6.8 Comparison to Literature

A comparison is made between the costs obtained in this study for the CO2-ECBMR base case 

design and those calculated using cost estimates from a paper by Wong, et al.5 The costs for this 
Enhanced Coalbed Methane

6-21

study were based on the EIA ‘Costs and Indices for Domestic Oil and Gas Field Equipment and 

Production Operations’ report14 and the API ‘Joint Association Survey on Drilling Costs’ 

report15. Wong, et al., cost estimates are given for a conceptual 50-well pair CO2-ECBMR field 

development in the Alberta Plains region and are shown in Table 6-11. It is to be noted that the 

well drilling and completion cost estimates are based on a reservoir depth of 1,280 m.

Table 6-11

CO2-ECBMR cost estimates from Wong, et al.5

Item Unit Value

CAPITAL COSTS

Wells:

Drilling $/well pair 346,840

Completion $/well pair 113,390

Equipment:

Tie-in $/well pair 113,390

Stimulation $/well pair 6,670

O&M COSTS

Well maintenance $/well pair/yr 21,344

Using these cost estimates, the capital, the well surface, and subsurface maintenance, costs of 

CO2-ECBMR were calculated for the 1,219 m case design. Table 6-12 shows the results of these 

calculations together with the previously determined CO2-ECBMR costs from this study. It 

should be noted that the latter, in order to make the results comparable, have been given for a 

reservoir depth of 1,219 m and do not include the front end lease expense of the sales gas 

compressor.

Table 6-12

Comparison of CO2-ECBMR cost results5,14

Item Unit This Study Using Cost 

Estimates

CAPITAL COSTS

Total cost of wells $ 63,600,000 62,131,000

Total cost of equipment $ 17,980,000 16,208,000

Subtotal $ 81,580,000 78,339,000

O&M COSTS

Well maintenance $/y 2,204,000 2,881,000

Subtotal $/y 7,660,000 not reported

This comparison shows that the cost of the CO2-ECBMR design is very similar for the two sets 

of cost data.
Enhanced CoalBed Methane

6-22

6.9 References

1 “Enhanced Oil Recovery Scoping Study,” EPRI, Palo Alto, CA. Tech. Rep. 113836, 1999.

2 Stevens, S.H., D. Spector and P. Riemer, “Enhanced coalbed methane recovery using CO2

injection: Worldwide resource and CO2 sequestration potential,” presented at the 1998 SPE 

International Conference and Exhibition, Beijing, China, May 2-6, 1998.

3 Reeves, S., “Geologic sequestration of CO2 in deep, unmineable coalbeds: An integrated 

research and commercial-scale field demonstration project,” presented at the First National 

Conference on Carbon Sequestration, Washington DC, May 14-17, 2001.

4 Energy Information Administration (EIA), “U.S. Crude Oil, Natural Gas, and Natural Gas 

Liquids Reserves 2000 Annual Report,” [Online document], Dec. 2001, [cited Feb. 2002], 

Available HTTP:

http://www.eia.doe.gov/oil_gas/natural_gas/data_publications/crude_oil_natural_gas_reserv

es/cr.html

5 Wong, S., W.D. Gunter and M.J. Mavor, “Economics of CO2 sequestration in coalbed 

methane reservoirs,” presented at the 2000 SPE/CERI Gas Technology Symposium,

Calgary, Alberta Canada, Apr. 3-5, 2000.

6 Hamelinck, C.N., A.P.C. Faaij, G.J. Ruijg, D. Jansen, H. Pagnier, F. van Bergen, K. Wolf, 

O. Barzandji, H. Bruining and H. Schreurs, “Potential for CO2 sequestration and enhanced 

coalbed methane production in the Netherlands,” [Online document], Mar. 2001, [cited 

Feb. 2002], Available HTTP: 

http://www.chem.uu.nl/nws/www/publica/e2001-07.pdf

7 http://www.spe-uk.org/uk/supporters/supportersfr.html

8 “Dewatering systems and techniques for coalbed methane wells,” GRI-81/0159, August 

1983.

9 “Enhanced coal bed methane recovery with CO2 sequestration,” IEA Greenhouse Gas R&D 

Programme, Report Number PH3/3, August 1998.

10 Pashin, J.C., et al, “Enhanced coalbed methane recovery through sequestration of carbon 

dioxide: Potential for a market-based environmental solution in the Black Warrior Basin of 

Alabama, First National Conference on Carbon Sequestration, May 14-17, 2001,

Washington, DC.

11 Reeves, Scott, e-mail communication, Jan. 5, 2002.

12 Wong, Sam, e-mail communication, Dec. 3, 2002.
Enhanced Coalbed Methane

6-23

13 Stevens, S.H., J.A Kuuskraa and R.A. Schraufnagel, “Technology spurs growth of U.S. 

coal-bed methane,” Oil & Gas Journal, Jan. 1, 1996.

14 Energy Information Administration (Office of Oil and Gas), “Costs and Indices for

Domestic Oil and Gas Field Equipment and Production Operations,” [Online document], 

Mar. 2000, [cited Jan. 2002], Available HTTP:

http://www.eia.doe.gov/oil_gas/natural_gas/data_publications/cost_indices/c_i.html

15 American Petroleum Institute – Policy Analysis and Statistics Department, “1998 Joint 

Association Survey on Drilling Costs,” [Online document], Nov. 1999, Available HTTP:

http://www.api.org/axs-api/products/joint.htm

7-1

7

DEPLETED GAS AND OIL RESERVOIR/AQUIFER 

STORAGE

7.1 Introduction

The CO2 sequestration options considered here include CO2 storage in depleted natural gas and 

oil reservoirs, and deep saline aquifers. Geologic CO2 storage options with value-added 

products, specifically EOR and ECBMR, are treated separately.

7.2 State of the Art

7.2.1 Applications

The first, and to date only, commercial-scale project dedicated to geologic CO2 storage is in 

operation at the Sleipner West field. Sleipner West is a natural gas/condensate field operated by 

Statoil and located in the North Sea about 250 km off the coast of Norway. The natural gas 

produced at the field has a CO2 content of about 9 percent which, to meet commercial 

specifications, must be reduced to 2.5 percent. It is standard practice in natural gas production 

for the byproduct CO2 to be vented to the atmosphere. At Sleipner, however, the CO2 is 

compressed and injected via a single well into the Utsira Formation, a 250-m-thick aquifer 

located at a depth of 800 m below the seabed. About one million tonnes of CO2 have been 

sequestered annually at Sleipner since October 1996, with a total of 20 million tonnes of CO2

expected to be sequestered over the lifetime of the project. A second scheme, which would 

involve about 0.7 million tonnes per year of CO2 produced at the Snohvit gas field in the Barents 

Sea off northern Norway being injected into a deep sub-sea formation, is planned.1,2,3,4,5

7.2.2 Storage Potential

Depleted Natural Gas Reservoirs

One type of geologic reservoir with significant potential for CO2 sequestration is the abandoned 

natural gas field. Nearly all of the volume of abandoned gas fields should be available for CO2

storage. The first reason for this is that the exploitation of a gas field norma lly extracts up to 95 

percent of the available gas. The second is that only a very small fraction of the abandoned 

reservoir’s pore space is likely to be invaded by formation water because water is more viscous 

than low-pressure methane. In the unlikely case that an abandoned reservoir does become water 

saturated, due to the reservoir being highly permeable and/or having been abandoned for many 
Depleted Gas and Oil Reservoir/Aquifer Storage

7-2

years prior to CO2 injection, the reservoir will be similar to an aquifer. It should be noted here 

that abandoned gas fields are quite widespread, with an estimated 98 to 133 giga tonnes of total 

carbon sequestration potential.1,2,6,7,8

In active natural gas fields, it has been proposed that CO2 injection could prolong the economic 

life of a field by maintaining reservoir pressure longer than would otherwise be possible. It is 

important to note that, to date, there have been no demonstrations of CO2-enhanced gas 

production. Furthermore, this technology is unlikely to be implemented in the future due to the 

fact that it risks contaminating the hydrocarbon reserve. For these reasons, CO2 injection into 

active gas fields is not considered here as a geologic CO2 storage option.1,6,7

Depleted Oil Reservoirs

The other type of hydrocarbon reservoir in which CO2 could possibly be sequestered is the 

depleted oil field. In the case of the depleted oil field, it is important to note that production 

ceases not because all the oil has been recovered but rather because the field is no longer 

economic to produce. It is typical for primary production to result in only about 30 percent of 

the original oil in place (OOIP) being recovered. Even in fields in which secondary recovery by 

waterflooding has taken place, around 50 percent of the OOIP may remain in the reservoir.

Deep Saline Aquifers

Deep saline aquifers have the greatest CO2 sequestration potential, with these reservoirs being 

the most widespread and having the largest volumes. The latter is very important given that, 

unlike exhausted hydrocarbon reservoirs where the reservoir pressure has been very substantially 

reduced by the production of reservoir fluids, the pressure in aquifers is hydrostatic or greater. In 

order to ensure that the fracture pressure of an aquifer is not exceeded, it is necessary that CO2

injection wells be located in regions of high permeability and that the total amount of CO2

injected be limited. Modeling suggests that an average of around only two percent of the pore 

volume of an aquifer can be safely occupied by CO2, but this number is highly uncertain.1

To give an illustration of the aquifer volumes needed to store the CO2 emissions from power 

plants, the aquifer volume required for the CO2 captured at the base-case IGCC plant was 

determined. Calculating the total quantity of CO2 to be supplied by the base-case plant over its 

20-year lifetime to be 43 Mt and assuming a supercritical CO2 density of 0.7 kg/m3, it is found 

that an aquifer with an effective pore volume of approximately 0.0617 km3 is needed. Given a 

realistic effective porosity of 30 percent and a storage efficiency of two percent, this is 

equivalent to a total aquifer volume of about 10.28 km3. This can be visualized as a circularshaped aquifer with a diameter of 11.4 km and a thickness of 100 m.1,8

CO2 can be sequestered in either of two types of deep saline aquifer. The first type of aquifer is 

directly analogous to a hydrocarbon field, where the reservoir acts as a geologic trap. It should 

also be possible to inject the CO2 into aquifers that do not have lateral seals. An impermeable 

caprock to prevent the buoyant CO2 from escaping vertically and a down-directed flow regime to 

transport the CO2 away from the surface should theoretically be sufficient to provide secure CO2
Depleted Gas and Oil Reservoir/Aquifer Storage

7-3

storage.9,10 This possibility vastly increases the total potential CO2 sequestration capacity of 

aquifers. A lack of information about aquifers that are not located near where oil and gas 

exploration has taken place has led to greatly varying estimates of global CO2 storage potential. 

An assessment of the varying estimates has, however, suggested that the global storage potential 

lies somewhere between 100 and 3,000 giga tonnes of carbon.1,8,11

7.2.3 Storage Mechanics

CO2 is stored in geologic formations by a number of different trapping mechanisms, with the 

exact mechanisms depending on the formation type. To make full use of storage capacity, the 

CO2 should be stored in a dense or supercritical phase, i.e., above the critical pressure of 7.4 

MPa. For a hydrostatic pressure gradient of 10.5 MPa/km, this condition is met at depths below 

about 700 m. At 800-m depth, the density of supercritical CO2 is 740 kg/m3. Since the CO2

under these pressure and temperature conditions will still be less dense than formation water, the 

CO2 will naturally rise to the top of the reservoir and a trap is needed to ensure that it does not 

reach the surface. In oil and gas reservoirs, as well as aquifers directly analogous to hydrocarbon 

fields, geologic traps immobilize the CO2. In the case of aquifers with no distinct geologic traps, 

however, an impermeable caprock above the underground reservoir is needed. This forces the 

CO2 to be entrained in the groundwater flow and is known as hydrodynamic trapping.1,7,8,10

Two other very important trapping mechanisms are solubility and mineral trapping. Solubility 

and mineral trapping involve the dissolution of CO2 into fluids, and the reaction of CO2 with 

minerals present in the host formation to form stable, solid compounds like carbonates, 

respectively. These latter two mechanisms are particularly important in the case of the aquifer 

with no lateral seals. As the CO2 moves through the reservoir along the flowpath, it comes into 

contact with uncarbonated formation water and reactive minerals. A proportion of the CO2

dissolves in the formation water and some of this dissolved CO2 becomes permanently fixed by 

reactions with minerals in the host rock. If the flowpath is long enough, the CO2 might all 

dissolve or become fixed by mineral reactions before it reaches the basin margin, essentially 

becoming permanently trapped in the reservoir.1,3,9,10

7.2.4 Storage Option Feasibility

The injection of CO2 into geologic formations is a promising CO2 sequestration option. First, the 

technology for injecting CO2 into exhausted hydrocarbon reservoirs and deep saline aquifers 

already exists. Oil producers in the Permian Basin of western Texas and eastern New Mexico, 

and in the Rocky Mountain and Mid-continent regions have been injecting CO2 for EOR for 

more than 25 years. Underground natural gas storage projects also provide a considerable base 

of relevant geologic and engineering experience. Second, even though no direct economic 

benefits are derived, it should be noted that the CO2 sequestration potential associated with the 

injection of CO2 into exhausted oil and natural gas reservoirs and deep saline aquifers greatly 

exceeds that of EOR and ECBMR using CO2 floods.7

Exhausted hydrocarbon reservoirs have the advantage over aquifers that these fields are proven 

long-term traps, their geology is well characterized, and their existing surface and subsurface 
Depleted Gas and Oil Reservoir/Aquifer Storage

7-4

infrastructures could readily be converted for CO2 distribution and injection. On the downside, 

the locating and sealing of abandoned wells could present an ongoing challenge. Aquifers, 

however, have a larger CO2 capacity, are more widespread and are generally located closer to 

CO2 emission sources.6,7,8,11

7.3 Process Description

Figure 7-1 is a block flow diagram, indicating the overall flow and distribution of CO2 from the 

IGCC plant to the depleted gas and oil fields, and aquifer. Developing a depleted reservoir or 

aquifer for CO2 storage involves:

• Screening and evaluation of sites

• Drilling and equipping injection wells

• Installation of high-pressure injection equipment and related piping

Equipment must be available at the injection site to accept pressurized CO2 from the pipeline, 

and transfer it to the injection well at the flow rate and pressure required for injection. The 

primary components include piping to distribute CO2 to the injection wells, CO2 flow controls 

equipment, and equipment to monitor well condition.

The source and quantity of CO2 supplied to the field is the same as for the EOR storage option, 

i.e. the system must be able to handle 3.76 million scm (7,389 tonnes) of CO2 per day. 

Determining the required CO2 pressure at the top of the well requires consideration of the 

pressure required at the bottom of the well to force CO2 into the injection zone, the pressure 

increase in the pipe due to the height of the CO2 column, and the pressure loss due to flow in the 

pipe. Moving the CO2 into the reservoir requires raising the CO2 sufficiently above the in situ 

pressure to provide a driving force, but not so high as to risk hydrofracturing the injection 

interval.12 It was decided that the pipeline delivery pressure, 103 bar, is adequate for injection.

The CO2 injection wells are as described in Section 5.3. For geologic CO2 storage, these wells 

are equipped with a battery of lease equipment that includes distribution lines, headers, electrical 

service and controls. This equipment enables the CO2 to be taken from the pipeline terminal and 

injected at a pressure that maintains the downhole injection point pressure.

As a final note, it is assumed that this facility, like the power plant, has a lifetime and 

design/construction time of 20 and 4 years, respectively.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-5

CO2Injection Wells

CO2 Pipeline

Terminal

152 bar

Boost

Compressor

2,650 kW

100 km

Pipeline,

12” diameter

83 bar

IGCC Plant

CO2 Source

Block Flow Diagram:

Depleted Oil & Gas, 

& Aquifer

7,389 tonne/day

CO2Injection Wells

7,389 tonne/day

Aquifer

Depleted Oil 

Reservoir

CO2Injection Wells

7,389 tonne/day

Depleted Gas 

Reservoir

Figure 7-1

Depleted oil, gas, and aquifer block flow diagram
Depleted Gas and Oil Reservoir/Aquifer Storage

7-6

7.4 Methodology Used

Depleted natural gas and oil reservoirs, and deep saline aquifers, differ quite substantially from 

one another in terms of typical values of reservoir parameters such as pressure, thickness, depth, 

and permeability. The processes that govern the rate at which CO2 can be injected at a well, and 

thus the number of wells required, are however essentially identical for the three types of 

reservoir. Given this, the same costing method is applied to each of the three geologic CO2

storage options.

The cost model for the geologic CO2 storage options can be broken down into a number of 

components. First, there is a relationship for calculating the number of wells required for a given 

CO2 flow rate, CO2 downhole injection pressure, and set of reservoir parameters. Second, an 

iterative procedure is used to take into account the interdependent relationship between CO2

downhole injection pressure and well number. Third, a set of capital and O&M cost factors are 

used to determine cost based on well number. Each of these components, illustrated in the 

overview diagram in Figure 7-2 is described below in greater detail.

Figure 7-2

Geologic storage cost model overview diagram

The value of a particular reservoir parameter, where the same type of geological reservoir is 

being considered, can vary significantly. This variation is important because it has the potential 

to greatly affect the cost estimate of the geologic CO2 storage option. In order to take account of 

this variation, base and sensitivity cases are run for each of the three storage options. These 

design bases, which comprise different sets of reservoir parameters, are detailed in Section 7.5.1.

INJECTIVITY MODEL 

Internal Calcs:

Reservoir temperature

CO2 viscosity

CO2 mobility

CO2 injectivity

Injection rate per well

Number of wells required

COST MODEL

Internal Calcs:

Capital costs:

Site screening & evaluation, injection 

equipment, injection wells

O&M costs:

Normal daily expenses, consumables, 

surface maintenance, subsurface 

maintenance

Inputs:

Reservoir depth

Capital charge rate

Outputs:

Total capital cost

Total O&M cost

Total annual cost 

Total cost per tonne CO2

Inputs:

CO2 mass flow rate

CO2 surface injection pressure

Reservoir pressure

Reservoir thickness

Reservoir depth

Reservoir permeability

PRESSURE CHANGE 

CALCULATION

Internal Calcs:

Well diameter

Gravity head

Friction loss

Pressure change

CO2 downhole 

injection pressure
Depleted Gas and Oil Reservoir/Aquifer Storage

7-7

7.4.1 Well Number Calculation

The well number calculation requires inputs for CO2 mass flow rate, CO2 downhole injection 

pressure, and reservoir pressure, thickness, depth, and permeability. Given the depth of the 

reservoir, reservoir temperature is calculated assuming a surface temperature of 15oC and a 

geothermal gradient of 25oC/km. The viscosity of the CO2 (μCO2) is then calculated based on a 

correlation published by McHugh and Krukonis.13 Next, the absolute permeability (ka) is found 

from

ka = (kh x kv)

0.5

where kv = the vertical permeability and is equal to 0.3 times the horizontal permeability and kh

= the given horizontal permeability. 14

A relationship, derived by Law and Bachu,14 is used to determine CO2 injectivity from CO2

mobility. This relationship is shown in Figure 7-3. The equation for CO2 injectivity is

CO2 injectivity = 0.0208 x CO2 mobility

where CO2 injectivity is equal to the mass flow rate of CO2 (m) that can be injected per unit of 

reservoir thickness (h) and per unit of downhole pressure difference (Pinj – Pres), and CO2

mobility equals the CO2 absolute permeability (ka) divided by the CO2 viscosity (μCO2). Given 

the CO2 injectivity, the CO2 injection rate per well (QCO2/well) can be found from

QCO2/well = CO2 injectivity x h x (Pinj – Pres)

Finally, the number of wells required (n) is given by

n = m/QCO2/well
Depleted Gas and Oil Reservoir/Aquifer Storage

7-8

CO2 Injectivity as a Function of CO2 Mobility

y = 0.0208x

0.10

1.00

10.00

100.00

10 100 1000 10000

CO2 mobility (md/mPa.s)

CO2 injectivity (t/d/m/MPa)

Figure 7-3

CO2 injectivity as a function of CO2 mobility

7.4.2 CO2 Downhole Injection Pressure Calculation

The surface CO2 injection pressure is equal to the 10.3 MPa minimum CO2 pipeline outlet 

pressure used in the design of the pipeline for CO2 transport. Based on this value, no additional 

recompression of CO2 is required at the wellhead. A typical well diameter of 0.059 m is used for 

the injection pipe.

An iterative procedure is used to calculate the CO2 downhole injection pressure and the required 

number of wells. This is because downhole injection pressure and well number are mutually 

dependent. Downhole injection pressure is found by adding the pressure increase due to the 

gravity head to the surface pressure and then subtracting from this the pressure decrease due to 

friction loss, which depends on the velocity of CO2 in each well. Well number, on the other 

hand, is determined by CO2 injectivity, which is dependent on the difference between the 

downhole injection and reservoir pressures.

It is important to note that, for the aquifer base and low-cost cases, it is necessary to increase the 

pipe diameter to 0.1 and 0.5 m, respectively. This is because smaller diameters in these cases 

result in unacceptable friction losses.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-9

7.4.3 Cost Calculations

The total capital cost comprises site screening and evaluation, injection equipment and well 

drilling costs, while the total O&M cost includes the costs of normal daily operations, 

consumables, and surface and subsurface maintenance.

The capital cost for site screening and evaluation is based on an estimate given in a recent study 

by the Battelle Memorial Institute.15 This study estimated the costs for preliminary site 

screening and candidate evaluation at $1,685,000.

All of the other costs, except for the well drilling cost, are calculated based on values given in the 

EIA ‘Costs and Indices for Domestic Oil and Gas Field Equipment and Production Operations’ 

report.12 Average lease equipment costs and O&M costs were developed on a per well basis. In 

the case of the injection equipment and surface maintenance, the average cost values are factored 

by 0.5 to take into account the number of wells and thus the size/complexity of the surface 

equipment. Similarly, the average cost value for subsurface maintenance is adjusted to take into 

account the well depth. These capital and O&M cost factors/functions are given in Table 7-1.

Table 7-1

Capital and O&M cost estimation factors/functions

Parameter Unit Value

CAPITAL COSTS

Injection Equipment 

(Flowlines & Connections)

$/well 43,600*(7,389/(280*Number_of_wells))^0.5

O&M COSTS

Normal Daily Expenses $/well 6,700

Consumables $/well 17,900

Surface Maintenance 

(Repair & Services)

$/well 13,600*(7,389/(280*Number_of_wells))^0.5

Subsurface Maintenance 

(Repair & Services)

$/well 5,000*Well_depth/1219

The well drilling cost is calculated based on a relationship derived from data contained in the 

‘1998 Joint Association Survey (JAS) on Drilling Costs’ report.16 This relationship between well 

depth and drilling cost is shown in Figure 7-4. To determine the relationship, regression analysis 

was performed on drilling cost data for onshore gas and oil wells. The total well drilling cost is 

found by multiplying the cost of drilling a single well for the given reservoir depth, taken from 

the graph, by the required number of wells.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-10

Well Drilling Cost as a Function of Depth

1998 Onshore Gas & Oil Well Data

y = 0.0888e0.0008x

0.10

1.00

10.00

0 1000 2000 3000 4000 5000 6000

Depth (m)

Cost ($M)

Figure 7-4

Well drilling cost as a function of depth

7.5 Design Basis

7.5.1 Reservoir Parameters and Well Numbers for Base and Sensitivity Cases

Typical, as well as a range of values for, reservoir properties of exhausted natural gas and oil 

fields, as suggested by Vello Kuuskraa of Advanced Resources International,17 are given in 

Table 7-2. It should be noted that these values are representative of the properties of gas and oil 

reservoirs found in the Permian Basin.

Table 7-2

Natural gas and oil reservoir property data

Parameter Units Gas 

Reservoir 

Typical

Gas 

Reservoir 

Range

Oil Reservoir 

Typical

Oil Reservoir 

Range

Pressure MPa 3.45 2.07 – 6.89 13.78 3.45 – 20.7

Thickness m 30.5 15.24 – 61.0 42.7 21.3 – 61.0

Depth m 1524 610 - 3048 1554 1524 – 2134

Permeability md 1 0.01 - 100 5 5 - 19
Depleted Gas and Oil Reservoir/Aquifer Storage

7-11

Based on these reservoir parameter values, a base case as well as high-cost and low-cost cases 

were selected for both the depleted gas and oil reservoir options. It should be noted that, in the 

case of the gas reservoir, the range of permeability values considered was limited to give a 

practical number of wells. The parameter values for each of the base, high-cost and low-cost 

cases, as well as the corresponding number of wells required, are given for the gas and oil 

reservoir storage options in Table 7-32 and Table 7-4, respectively.

Table 7-3

Design ba ses for natural gas reservoir storage option

Parameter Units Gas Reservoir

Base Case

Gas Reservoir

High Cost Case

Gas Reservoir

Low Cost Case

Pressure MPa 3.5 6.9 2.1

Thickness m 31 15 61

Depth m 1,524 3,048 610

Permeability md 1 0.8 10

Injection Rate per Well* t/d 154 58 2,985

Number of Wells* 48 127 3

* calculated

Table 7-4

Design bases for oil reservoir storage option

Parameter Units Oil Reservoir

Base Case

Oil Reservoir

High Cost Case

Oil Reservoir

Low Cost Case

Pressure MPa 13.8 20.7 3.5

Thickness m 43 21 61

Depth m 1,554 2,134 1,524

Permeability md 5 5 19

Injection Rate per Well* t/d 358 116 5,720

Number of Wells* 21 64 2

* calculated

The aquifer base and sensitivity cases are based on aquifer property data given in the 

literature,8,14 including data obtained from the Bureau of Economic Geology’s website.18 This 

property data is presented in Table 7-5. In addition, the calculated values of well number are 

shown. It can be seen from this table that aquifer properties, and thus the number of wells that 

would be required and the cost, can vary considerably.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-12

Table 7-5

Aquifer property data

Aquifer Pressure

(MPa)

Thickness

(m)

Depth

(m)

Permeability

(md)

Number of 

wells

Sleipner West 9.0 184 1,020 10 2

IEA Study Aquifer 11.3 55 1,459 13 6

Glauconitic Sandstone, 

Albert Basin

12.4 13 1,480 30 11

Repetto Formation, Los 

Angeles Basin

6.9 800 2,400 250 1

Arbuckle Group, Oklahoma 2.1 600 2,400 0.005 418

Paluxy Sandstone, East 

Texas Basin

10.3 75 1,000 400 1

Jasper Interval, East Texas 

Gulf Coast

8.4 1,500 800 100 1

Pottsville Formation, Black 

Warrior Basin

6.9 1,100 500 15 1

Cedar Key Dolomite, 

Central Florida Region

1.0 325 1,000 15 1

Incomplete sets of data for 13 aquifers were also available from the Bureau of Economic 

Geology. This data is shown in Table 7-6.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-13

Table 7-6

Incomplete sets of aquifer property data

Aquifer Pressure

(MPa)

Thickness

(m)

Depth

(m)

Permeability

(md)

Glen Canyon Group, Sevier/Kaiparowitz 

Basin

175 2,000

Morrison Formation, San Juan Basin 250 1,600

Fox Hills Sandstone, Power River Basin 175 800

Madison Group, Williston Basin 250 1,400

Lyons Sandstone, Denver Basin 300 2,000

Granite Wash, Palo Duro Basin 9.1 800 1,000

Woodbine Formation, East Texas Basin 12.4 100 1,000

Frio Formation, Texas Gulf Coast 500 800 500

St. Peter Sandstone, Illinois Basin 5.3 50 50

Mt. Simon Formation, Michigan Basin 11.4 100 100

Tuscaloosa Group, Alabama Gulf Coastal 

Plain

10.9 40 40

Oriskany Formation, Appalachian Basin 10.0 5 5

Lower Potomac Group, Eastern Coastal 

Plain

6.9 225 225

The data given in these two tables were used in a statistical analysis to determine suitable base, 

high-cost and low-cost cases for the aquifer storage option. The calculation of the values of 

aquifer pressure and depth for the base, high-cost and low-cost cases used standard statistical 

functions. In the case of aquifer thickness and permeability, however, a logarithmic regression 

was necessary. This was due to the fact that the values of these two latter variables varied by 

more than two orders of magnitude.

The base values for aquifer pressure and depth were based on the arithmetic mean. The highcost values of each of these parameters were then taken as the mean plus the standard deviation 

and the low-cost values as the mean minus the standard deviation. This corresponds to an 

increase in pressure and depth causing an increase in cost, and a decrease in these parameters 

causing a reduction in cost. The values for thickness and permeability were calculated in a 

similar manner, but taking into account tha t a reduction in these parameters increases the cost 

and an increase reduces the cost. The final value for each parameter for each of the base, highcost and low-cost cases is given Table 7-7. The number of wells required for each of these cases 

is also shown.
Depleted Gas and Oil Reservoir/Aquifer Storage 

7-14 

Table 7-7 

Design bases for aquifer storage option 

Parameter Units Aquifer 

Base Case 

Aquifer 

High Cost Case 

Aquifer 

Low Cost Case 

Pressure MPa 8.4 11.8 5.0 

Thickness m 171 42 703 

Depth m 1,239 1,784 694 

Permeability md 22 0.8 585 

Injection Rate per Well* t/d 9,363 82 889,495 

Number of Wells* 1 91 1 

* calculated 

Reservoirs that are thick, shallow, and have high permeability require a smaller number of wells 

and, therefore, have a lower storage cost. A higher reservoir pressure results in lower injectivity 

(not desired), but higher CO2 densities (desired). Therefore, a moderate pressure is optimal. In 

general, the permeability is the most critical parameter in determining costs. 

It is important to note that the injection rate per well of 889,495 tonnes of CO2 per day for the 

low cost case was calculated without setting any limit on the well diameter. Indeed, this 

injection rate requires a well diameter of 0.5 m, which is too large to be used in practice. Given 

that the standard well diameter used for the other cases for each of the geologic options is 0.059 

m, it would seem reasonable to limit the diameter to double this at 0.120 m. This would give a 

maximum flow rate of around 25,100 tonnes of CO2 per day. 

7.5.2 Capital and O&M Cost Inputs 

The capital cost of site screening and evaluation is assumed equal ( $1,685,000) for each of the 

three geologic storage options. The other capital and O&M costs are calculated using the cost 

estimation factors/functions given in Section 7.4.3. Based on the respective values of well depth 

and required number of wells, as detailed in Section 7.5.1, it is possible to determine the costs on 

a per well basis for each of the storage options. These per well costs are then multiplied by the 

total number of wells. The model inputs for the capital and O&M costs for the base cases are 

given in Table 7-8. 
Depleted Gas and Oil Reservoir/Aquifer Storage

7-15

Table 7-8

Capital and O&M cost inputs for gas and oil reservoir, and aquifer base cases

Parameter Gas Reservoir Oil Reservoir Aquifer

Number of Wells 48 21 1

CAPITAL COSTS

Screening and Evaluation of 

Sites

$1,685,000 $1,685,00 $1,685,000

Injection Equipment

(Flowlines & Connections)

$1,552,000 $1,026,000 $224,000

Injection Wells $14,426,000 $6,465,000 $239,000

Subtotal $17,700,000 $9,180,000 $2,150,000

O&M COSTS

Normal Daily Expenses $322,000 $141,000 $7,000

Consumables $859,000 $376,000 $18,000

Surface Maintenance

(Repair & Services)

$484,000 $320,000 $70,000

Subsurface Maintenance

(Repair & Services)

$300,000 $134,000 $5,000

Subtotal $1,970,000 $970,000 $100,000

7.6 Model Results

This section presents costs for CO2 capture and storage for the base cases described in Section 

7.5. The storage costs comprise transaction, transportation, injection and monitoring costs. 

These costs are combined with capture costs from an IGCC power plant. The results are given 

on several bases as described in Chapter 2. Appendix B provides a detailed description of these 

spreadsheets.

7.6.1 Depleted Gas Reservoir: Spreadsheet Orientation and Typical Costs

The Process Input sheet for the depleted gas reservoir base case is presented in Table 7-9.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-16

Table 7-9

Process input sheet for base case depleted gas reservoir

Design Basis Economic

IGCC Plant IGCC Plant (Case 3a)

Capacity Factor 80% CO2 Capture Cost ($/tonne captured) $14.55

Pipeline Distance 100 km COE Including CO2 Capture Cost ($/MWh) $55.08

Pipeline Distance 62 miles

Nominal pipe size (in) 12 IGCC Plant & CO 2 Storage

After-Tax Discount Rate 6.09%

CO 2 Storage Levelized Carrying Charge Factor 15.0%

Design Basis Base Cost Case

Well Depth (m) 1,524

Reservoir Pressure (MPa) 3.5

Thickness (m) 31

Permeability (md) 1.0

Injection Rate Per Well (tonne/day) 156

Depleted Gas Reservoir Input Variables

The Summary sheet for the depleted gas reservoir base case is presented in Table 7-10.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-17

Table 7-10

Summary sheet for base case depleted gas reservoir

Process Description Capital & O&M Cost

Pileline Distance 100 km CO2 Stored (M tonne/yr) 2.158 Capital O&M

Design Basis: Base Cost Case CO2 Avoided (M tonne/yr) 1.822 Transaction $2,160,000 $0

LC Avoided GHG (M tonne/yr) 2.154 Transport $26,800,000 $1,333,000

LC Avoided GHG (M tonne/yr) 1.804 Sequestration $17,700,000 $1,970,000

Monitoring $0 $216,000

Economic Description

After-Tax Discount Rate 6.09% Total $46,700,000 $3,500,000

1. Levelized Annual CO2 Storage Costs (years 1-20) CO2 Captured Basis LC Avoided GHG Basis

(storage only)

CO2 Storage Cost, M $/yr 10.48 10.48

CO2 Stored or Avoided, M tonne/yr 2.16 2.15

Storage Cost, $/tonne CO2 4.86 4.87

2. Levelized Annual CO2 Capture and Storage Costs (years 1-20) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

CO2 Capture Cost, M $/yr 31.39 31.39 31.39

CO2 Storage Cost, M $/yr 10.48 10.48 10.48

Capture + Storage Cost, M$/yr 41.87 41.87 41.87

CO2 Stored or Avoided, M tonne/yr 2.16 1.82 1.80

Capture + Storage Cost, $/tonne CO2 19.41 22.98 23.21

3. CO2 Capture and Net Storage Costs, NPV Basis (years 1-100) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

NPV of CO2 Capture Costs, M$ 357.42 357.42 357.42

NPV of CO2 Storage Costs, M$ 120.45 120.45 120.45

NPV of Capture + Storage Costs, M$ 477.87 477.87 477.87

NPV of CO2 Stored or Avoided, M tonne 24.57 20.75 20.55

Capture + Storage Cost (NPV Basis), $/tonne CO2 19.45 23.03 23.26

4. Cost of Electricity, $/MWh

Reference Plant (no capture) 43.98

Capture Costs (years 1-20) 11.10

Storage Costs (years 1-20) 3.71

Storage Costs, Monitoring, etc. (years 21-100) 0.03

Total Cost of Electricity 58.82

Depleted Gas Reservoir Summary

Cost of CO2 Capture & Storage

7.6.2 Depleted Oil Reservoir: Spreadsheet Orientation and Typical Costs

The Process Input sheet for the depleted oil reservoir base case is presented in Table 7-11.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-18

Table 7-11

Process input sheet for depleted oil reservoir base case 

The Summary sheet for the depleted oil reservoir base case is presented in Table 7-12.

Design Basis Economic

IGCC Plant IGCC Plant (Case 3a)

Capacity Factor 80% CO2 Capture Cost ($/tonne captured) $14.55

Pipeline Distance 100 km COE Including CO2 Capture Cost ($/MWh) $55.08

Pipeline Distance 62 miles

Nominal pipe size (in) 12 IGCC Plant & CO 2 Storage

After-Tax Discount Rate 6.09%

CO 2 Storage Levelized Carrying Charge Factor 15.0%

Design Basis Base Cost Case

Well Depth (m) 1,554

Reservoir Pressure (MPa) 13.8

Thickness (m) 43

Permeability (md) 5.0

Injection Rate Per Well (tonne/day) 360

Depleted Oil Reservoir Input Variables
Depleted Gas and Oil Reservoir/Aquifer Storage

7-19

Table 7-12

Summary sheet for depleted oil reservoir base case 

Process Description Capital & O&M Cost

Pileline Distance 100 km CO2 Stored (M tonne/yr) 2.158 Capital O&M

Design Basis: Base Cost Case CO2 Avoided (M tonne/yr) 1.822 Transaction $2,160,000 $0

LC Avoided GHG (M tonne/yr) 2.154 Transport $26,800,000 $1,333,000

LC Avoided GHG (M tonne/yr) 1.804 Sequestration $9,180,000 $970,000

Monitoring $0 $216,000

Economic Description

After-Tax Discount Rate 6.09% Total $38,140,000 $2,520,000

1. Levelized Annual CO2 Storage Costs (years 1-20) CO2 Captured Basis LC Avoided GHG Basis

(storage only)

CO2 Storage Cost, M $/yr 8.22 8.22

CO2 Stored or Avoided, M tonne/yr 2.16 2.15

Storage Cost, $/tonne CO2 3.81 3.82

2. Levelized Annual CO2 Capture and Storage Costs (years 1-20) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

CO2 Capture Cost, M $/yr 31.39 31.39 31.39

CO2 Storage Cost, M $/yr 8.22 8.22 8.22

Capture + Storage Cost, M$/yr 39.61 39.61 39.61

CO2 Stored or Avoided, M tonne/yr 2.16 1.82 1.80

Capture + Storage Cost, $/tonne CO2 18.36 21.74 21.95

3. CO 2 Capture and Storage Costs, NPV Basis (years 1-100) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

NPV of CO2 Capture Costs, M$ 357.42 357.42 357.42

NPV of CO2 Storage Costs, M$ 94.71 94.71 94.71

NPV of Capture + Storage Costs, M$ 452.14 452.14 452.14

NPV of CO2 Stored or Avoided, M tonne 24.57 20.75 20.55

Capture + Storage Cost (NPV Basis), $/tonne CO2 18.40 21.79 22.01

4. Cost of Electricity, $/MWh

Reference Plant (no capture) 43.98

Capture Costs (years 1-20) 11.10

Storage Costs (years 1-20) 2.91

Storage Costs, Monitoring, etc. (years 21-100) 0.03

Total Cost of Electricity 58.02

Depleted Oil Reservoir Summary

Cost of CO2 Capture & Storage
Depleted Gas and Oil Reservoir/Aquifer Storage

7-20

7.6.3 Aquifer Storage: Spreadsheet Orientation and Typical Costs

The Process Input sheet for the aquifer storage base case is presented in Table 7-13.

Table 7-13

Process input sheet for aquifer storage base 

Design Basis Economic

IGCC Plant IGCC Plant (Case 3a)

Capacity Factor 80% CO2

 Capture Cost ($/tonne captured) $14.55

Pipeline Distance 100 km COE Including CO2 Capture Cost ($/MWh) $55.08

Pipeline Distance 62 miles

Nominal pipe size (in) 12 IGCC Plant & CO2

 Storage

After-Tax Discount Rate 6.09%

CO 2 Storage Levelized Carrying Charge Factor 15.0%

Design Basis Base Cost Case

Well Depth (m) 1,239

Reservoir Pressure (MPa) 8.4

Thickness (m) 171

Permeability (md) 22.0

Injection Rate Per Well (tonne/day) 6,452

Aquifer Storage Input Variables

The Summary sheet for the aquifer storage base case is presented in Table 7-14.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-21

Table 7-14

Summary sheet for the aquifer storage base case 

Process Description Capital & O&M Cost

Pileline Distance 100 km CO2 Stored (M tonne/yr) 2.158 Capital O&M

Design Basis: Base Cost Case CO2 Avoided (M tonne/yr) 1.822 Transaction $2,160,000 $0

LC Avoided GHG (M tonne/yr) 2.155 Transport $26,800,000 $1,333,000

LC Avoided GHG (M tonne/yr) 1.804 Sequestration $2,480,000 $158,000

Monitoring $0 $216,000

Economic Description

After-Tax Discount Rate 6.09% Total $31,440,000 $1,710,000

1. Levelized Annual CO2 Storage Costs (years 1-20) CO2 Captured Basis LC Avoided GHG Basis

(storage only)

CO2 Storage Cost, M $/yr 6.41 6.41

CO2 Stored or Avoided, M tonne/yr 2.16 2.15

Storage Cost, $/tonne CO2 2.97 2.98

2. Levelized Annual CO2 Capture and Storage Costs (years 1-20) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

CO2 Capture Cost, M $/yr 31.39 31.39 31.39

CO2 Storage Cost, M $/yr 6.41 6.41 6.41

Capture + Storage Cost, M$/yr 37.80 37.80 37.80

CO2 Stored or Avoided, M tonne/yr 2.16 1.82 1.80

Capture + Storage Cost, $/tonne CO2 17.52 20.74 20.95

3. CO2 Capture and Net Storage Costs, NPV Basis (years 1-100) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

NPV of CO2 Capture Costs, M$ 357.42 357.42 357.42

NPV of CO2 Storage Costs, M$ 74.08 74.08 74.08

NPV of Capture + Storage Costs, M$ 431.50 431.50 431.50

NPV of CO2 Stored or Avoided, M tonne 24.57 20.75 20.55

Capture + Storage Cost (NPV Basis), $/tonne CO2 17.56 20.80 21.00

4. Cost of Electricity, $/MWh

Reference Plant (no capture) 43.98

Capture Costs (years 1-20) 11.10

Storage Costs (years 1-20) 2.27

Storage Costs, Monitoring, etc. (years 21-100) 0.03

Total Cost of Electricity 57.38

Aquifer Storage Summary

Cost of CO2 Capture & Storage

7.7 Sensitivity Analyses

7.7.1 Depleted Gas Reservoir

The sensitivity of the CO2 storage cost for the depleted gas reservoir option is determined for 

five key parameters: well depth, pressure, thickness, permeability and pipeline distance. It can 

be seen in Figure 7-5 that increases in well depth, reservoir pressure and pipeline distance cause 

an increase in the cost of storage, while increases in reservoir thickness and permeability 

decrease the storage cost. More noteworthy, the figure shows that, for the chosen base case 

values, changes in thickness and permeability have the greatest effect on storage cost.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-22

Gas Reservoir Cost Sensitivity

Levelied Annual CO2 Storage Costs (LC Avoided GHG Basis)

3.00

3.50

4.00

4.50

5.00

5.50

6.00

6.50

7.00

-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%

% Change in Variable

$/tonne CO2 eq. LC GHG avoided

Well Depth

Pressure

Thickness

Permeability

Pipeline Distance

Figure 7-5

Sensitivity analysis for depleted gas reservoir

High and low cost cases have been chosen for the depleted gas reservoir option, and are 

presented together with the base case in Table 7-15. For the high and low cost values for each of 

the five key parameters, the percentage change in the value from the base case is shown. This is 

done to illustrate the fact that the range in the values of some parameters is expected to be greater 

than for others.

Table 7-15

Depleted gas reservoir base, high cost and low cost cases

Parameter Units Gas Reservoir

Base Case

Gas Reservoir

High Cost Case

Gas Reservoir

Low Cost Case

Pressure MPa 3.5 6.9 +97% 2.1 -40%

Thickness m 31 15 -52% 61 +97%

Depth m 1,524 3,048 +100% 610 -60%

Permeability md 1 0.8 -20% 10 +900%

Pipeline Distance km 100 300 +200% 0 -100%
Depleted Gas and Oil Reservoir/Aquifer Storage

7-23

The results for the high and low cost cases as well as the base case are given in Table 7-16. The 

range of CO2 storage cost for the depleted gas reservoir option varies by about a factor of 20 for 

the high and low cost cases.

Table 7-16

Results for depleted gas reservoir base, high cost and low cost cases

Parameter Units Gas 

Reservoir

 Base Case

Gas Reservoir 

High Cost Case

Gas 

Reservoir 

Low Cost 

Case

Injection Rate per Well t/d 156 57 2,975

Number of Wells 48 129 3

Levelized Annual CO2 Storage 

Cost 

$/tonne CO2

Eq. LC GHG 

avoided

4.87 19.43 1.20

7.7.2 Depleted Oil Reservoir

The sensitivity of the CO2 storage cost for the depleted gas reservoir option is determined for the 

same five key parameters as used in the case of the depleted oil reservoir. It can be seen in 

Figure 7-6 that increases in reservoir pressure and pipeline distance cause an increase in the cost 

of storage, while increases in well depth, reservoir thickness and permeability decrease the 

storage cost. In contrast to the depleted gas reservoir option, increased well depth results in a

decrease in the number of wells required, the resulting decrease in cost of which outweighs the 

increase in well drilling cost. The figure shows that, for the chosen base case values, changes in 

pressure have the greatest effect on storage cost.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-24

Oil Reservoir Cost Sensitivity

Levelized Annual CO2 Storage Costs (LC Avoided GHG Basis)

2.00

2.50

3.00

3.50

4.00

4.50

5.00

5.50

6.00

6.50

7.00

-50% -40% -30% -20% -10% 0% 10% 20% 30% 40% 50%

% Change in Variable

$/tonne CO2 eq. LC GHG avoided

Well Depth

Pressure

Thickness

Permeability

Pipeline Distance

Figure 7-6

Sensitivity analysis for depleted oil reservoir

High and low cost cases have been chosen for the depleted oil reservoir option, and are presented 

together with the base case in Table 7-17. For the high and low cost values for each of the five 

key parameters, the percentage change in the value from the base case is shown.

Table 7-17

Depleted oil reservoir base, high cost and low cost cases

Parameter Units Oil Reservoir

Base Case

Oil Reservoir

High Cost Case

Oil Reservoir

Low Cost Case

Pressure MPa 13.8 20.7 +50% 3.5 -75%

Thickness m 43 21 -51% 61 +42%

Depth m 1,554 2,134 +37% 1,524 -29%

Permeability md 5 5 0% 19 +280%

Pipeline

Distance

km 100 300 +200% 0 -100%

The results for the high and low cost cases as well as the base case are given in Table 7-18. The 

range of the CO2 storage cost for the depleted oil reservoir option differ by about a factor of 10 

for the high and low cost cases. 
Depleted Gas and Oil Reservoir/Aquifer Storage

7-25

Table 7-18

Results for depleted oil reservoir base, high cost and low cost cases

Parameter Units Oil 

Reservoir

 Base Case

Oil Reservoir 

High Cost 

Case

Oil Reservoir 

Low Cost Case

Injection Rate per Well t/d 360 115 5,690

Number of Wells 21 65 2

Levelized Annual CO2 Storage 

Cost 

$/tonne CO2 

Eq. LC GHG 

avoided

3.82 11.16 1.21

7.7.3 Aquifer

The sensitivity of the CO2 storage cost for the aquifer option is determined for the same five key 

parameters as for the other geologic storage options. It can be seen in Figure 7-7 that, for the 

range of values considered, the storage cost is only sensitive to pipeline distance. This is 

because, for the base case values chosen, the value of CO2 injectivity is very high. This in turn 

results in only one well being required, where this is relatively insensitive to changes in reservoir 

properties.

Aquifer Cost Sensitivity

Levelized Annual CO2 Storage Costs (LC Avoided GHG Basis)

1.75

2.25

2.75

3.25

3.75

4.25

-50% -30% -10% 10% 30% 50%

% Change in Variable

$/tonne CO2 eq. LC GHG avoided

Well Depth

Pressure

Thickness

Permeability

Pipeline Distance

Figure 7-7

Sensitivity analysis for aquifer
Depleted Gas and Oil Reservoir/Aquifer Storage

7-26

High and low cost cases have been chosen for the aquifer option, and are presented together with 

the base case in Table 7-19. For the high and low cost values for each of the five key 

parameters, the percentage change in the value from the base case is shown. As compared to the 

depleted gas and oil reservoir options, thickness and permeability can vary to a far greater 

degree.

Table 7-19

Aquifer base, high cost and low cost cases

Parameter Units Aquifer

Base Case

Aquifer

High Cost Case

Aquifer

Low Cost Case

Pressure MPa 8.4 11.8 +40% 5.0 -40%

Thickness m 171 42 -75% 703 +311%

Depth m 1,239 1,784 +44% 694 -44%

Permeability md 22 0.8 -96% 585 +2559%

Pipeline 

Distance

km 100 300 +200% 0 -100%

The results for the high and low cost cases as well as the base case are given in Table 7-20. The 

range of CO2 storage cost for the aquifer option differ by about a factor of 10 for the high and 

low cost cases.

Table 7-20

Results for aquifer base, high cost and low cost cases

Parameter Units Aquifer

 Base 

Case

Aquifer 

High Cost 

Case

Aquifer 

Low Cost 

Case

Injection Rate per Well t/d 9,363 82 889,495

Number of Wells 1 91 1

Levelized Annual CO2 Storage 

Cost 

$/tonne CO2

Eq. LC GHG 

avoided

2.93 11.71 1.14

It is important to note that an aquifer could have a value of permeability equal to or 2 or 3 orders 

of magnitude less than the base case value. A reduction in the permeability of this magnitude 

would in turn cause a dramatic increase in the CO2 storage cost. For example, for the base case, 

reducing the base case permeability value from 22 to 0.22 md gives a storage cost of $5.37, 

while a permeability of 0.022 md gives a cost of $25.23. Similarly, as the thickness of the 

reservoir approaches zero, the storage cost skyrockets.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-27

7.8 Comparison To Literature

7.8.1 Studies Used in Model Evaluation

Injection scheme details and reservoir properties as well as cost data were collected from the 

studies listed in Table 7-21. All of the studies are concerned with the injection of CO2 into a 

saline aquifer, except for the ‘IEA depleted reservoir’ study that looks at storing CO2 in an 

exhausted gas reservoir.

Table 7-21

Injection schemes’ characteristics

Study CO2

Flow 

Rate 

(t/d)

Downhole 

Injection 

Pressure 

(MPa)

Reservoir 

Pressure 

(MPa)

Thickness 

(m)

Depth 

(m)

Permeability 

(md)

Number 

of Wells

Location

IEA aquifer810,685 28.0 11.3 55 1,459 13 6 Onshore

IEA depleted 

reservoir8

8,560 10.4 3.0 - 2,500 100 4 Onshore

Elsamprojekt19 3,770 - - - 1,100 - 12 Onshore

GEODISC20 15,780 17.4 17.2 400 1,600 300 4 Offshore

Sleipner 

West21

2,740 11.0 9.0 184 1,020 10 1 Offshore

The aquifer in both the ‘GEODISC’ and ‘Sleipner West’ studies is located offshore, in a depth of 

water of 100 and 80 m respectively. Due to certain reservoir properties not being specified, a 

benchmark for CO2 injectivity cannot be obtained from either of the ‘IEA depleted reservoir’ or 

the ‘Elsamprojekt’ studies. It should also be noted that cost data are not available for the 

‘Sleipner West’ project.

7.8.2 Comparison of Values from Model and Studies

Figure 7-8 shows that, for CO2 injectivity, the value calculated by the model generally agrees 

with the value used in each of the studies. Indeed, the same relationship between CO2 mobility 

and injectivity, as is used in the model, has been used in the ‘GEODISC’ study22 and possibly 

also the ‘IEA aquifer’ study. In the case of the ‘Sleipner West’ study, it should be noted the 

model at least underestimates, rather than overestimates, the injectivity.
Depleted Gas and Oil Reservoir/Aquifer Storage

7-28

CO2 Injectivity as a Function of CO2 Mobility

y = 0.0208x

0.10

1.00

10.00

100.00

10 100 1000 10000

CO2 mobility (md/mPa.s)

CO2 injectivity (t/d/m/MPa)

IEA aquifer study

GEODISC

Sleipner West

'Law and Bachu' values

 

Figure 7-8

Comparison of CO2 injectivity values

A comparison of well drilling cost as calculated by the model and given in various studies is 

shown in Figure 7-9. The drilling cost in three of the studies can be seen to be about four times 

that calculated by the model, with the value in the ‘GEODISC’ study being exceedingly higher. 

The significant difference between the model’s and the ‘GEODISC’ study’s drilling cost can be 

attributed in part to the aquifer in the latter being located offshore. The drilling costs in the other 

three studies, one of which is based on conditions in Europe and the other two on those in 

Canada, are likely higher due to less drilling activity in these regions.

 Well Drilling Cost as a Function of Depth

1998 Onshore Gas & Oil Well Data

y = 0.0888e0.0008x

0.01

0.10

1.00

10.00

100.00

0 1000 2000 3000 4000 5000 6000

Depth (m)

Cost ($M)

IEA aquifer

IEA depleted reservoir

Elsamprojekt

GEODISC

1998 drilling cost data

1998 cost data * 2

1998 cost data * 4

Figure 7-9

Comparison of onshore well drilling cost values
Depleted Gas and Oil Reservoir/Aquifer Storage

7-29

Injecting CO2 into an offshore reservoir can be expected to be significantly more expensive than 

for a reservoir in an onshore location. Firstly, offshore drilling costs are higher. This can be 

seen from a comparison of Figure 7-10, which gives the cost of offshore well drilling based on 

1998 JAS data for offshore gas and oil wells, with Figure 7-9. From Figure 7-10 it can also be 

seen that the offshore well drilling cost given in the ‘GEODISC’ study is about four times the 

JAS value. Secondly, CO2 injection into an offshore reservoir requires that a platform be 

installed. The cost of a platform depends primarily on the water depth and the number of wells it 

accommodates, with an unmanned platform with ten wells in a water depth of around 100 and 

200 m costing around $4 million and $6.5 million per meter water depth, respectively20. If CO2

injection into offshore reservoirs were to be considered, these factors would need to be taken into 

account.

Well Drilling Cost as a Function of Depth

1998 Offshore Gas and Oil Well Data

y = 1.3555e0.0004x

0.01

0.10

1.00

10.00

100.00

0 1000 2000 3000 4000 5000 6000

Depth (m)

Cost ($M)

GEODISC

1998 drilling cost data

1998 cost data * 2

1998 cost data * 4

Figure 7-10

Comparison of offshore well drilling cost values
Depleted Gas and Oil Reservoir/Aquifer Storage

7-30

7.9 References

1 Holloway, S., J.P. Heederik, L.G.H. van der Meer, I. Czernichowski-Lauriol, R. Harrison, 

E. Lindeberg, I.R. Summerfield, C. Rochelle, T. Schwarzkopf, O. Kaarstad and B. Berger, 

“The underground disposal of carbon dioxide,” Summary report for the Joule II Project 

CT92-0031, 1996.

2 Royal Commission on Environmental Pollution, “Energy – The changing climate,” [Online 

document], Jun. 2000, [cited Mar. 2002], Available HTTP:

http://www. Rcep.org.uk/newenergy.html

3 Herzog, H., “What future for carbon capture and sequestration?” Environmental Science and 

Technology, 35:7, pp. 148A-153A, Apr. 1, 2001.

4 International Energy Agency Greenhouse Gas R&D Programme (IEAGHG), “CO2 storage 

in saline aquifers,” [Online document], Jan. 2002, [cited Mar. 2002], Available HTTP:

http://www.ieagreen.org.uk/sacshome.htm

5 Kaarstad, Olav, e-mail communication, Sep. 29, 2001.

6 Stevens, S., V. Kuuskraa and J. Gale, “Sequestration of CO2 in depleted oil and gas fields: 

Global capacity, costs and barriers,” presented at the Fifth International Conference on 

Greenhouse Gas Control Technologies (GHGT-5), Cairns, Australia, Aug. 13-16, 2000.

7 U.S. Department of Energy (Offices of Science and Fossil Energy), “Carbon sequestration –

Research and development,” [Online document], Dec. 1999, [cited Mar. 2002], Available 

HTTP:

http://www.ornl.gov/carbon_sequestration/

8 Ormerod, W., “The disposal of carbon dioxide from fossil fuel fired power stations,” Tech. 

Rep. IEAGHG/SR3, Jun. 1994.

9 Gunter, W.D., Bachu, S., Law, D.H.-S., Marwaha, V., Drysdale, D.L., Macdonald, D.L. and 

T.J. McCann, “Technical and economic feasibility of CO2 disposal in aquifers within the 

Alberta sedimentary basin,” Energy Convers. Mgmt., 37:6-8, pp. 1135-1142, 1996.

10 Hitchon, B. (editor), “Aquifer disposal of carbon dioxide: Hydrodynamics and mineral

trapping – proof of concept,” 1996.

11 Stevens, S. and J. Gale, “Geologic CO2 sequestration may benefit upstream industry,” Oil 

and Gas Journal, May 15, 2000.

12 Energy Information Administration – Office of Oil and Gas, “Costs and Indices for

Domestic Oil and Gas Field Equipment and Production Operations,” [Online document], 

Mar. 2000, Available HTTP:
Depleted Gas and Oil Reservoir/Aquifer Storage

7-31

http://www.eia.doe.gov/oil_gas/natural_gas/data_publications/cost_indices/c_i.html

13 McHugh, M. and V. Krukonis, “Supercritical fluid extraction: principles and practice,” 

1986.

14 Law, D. and S. Bachu, “Hydrogeological and numerical analysis of CO2 disposal in deep 

aquifers in the Alberta sedimentary basin,” Energy Convers. Mgmt., 37:6-8, pp. 1167-1174, 

1996.

15 Smith, L.A. et al, “Engineering and Economic Assessment of Carbon Dioxide Sequestration 

in Saline Formations,” presented at the First National Conference on Carbon Sequestration, 

May 14-17, 2001, Washington D.C.

16 American Petroleum Institute – Policy Analysis and Statistics Department, “1998 Joint 

Association Survey on Drilling Costs,” [Online document], Nov. 1999, Available HTTP:

http://www.api.org/axs-api/products/joint.htm

17 Kuuskraa, Vello, e-mail communication, Mar. 28, 2001.

18 Bureau of Economic Geology (The University of Texas at Austin), “Carbon dioxide 

sequestration – Study areas,” [Online document], 2000, [cited Dec. 2001], Available HTTP:

http://www.beg.utexas.edu/environqlty/co2seq/co2data.htm

19 Krom, T.D., F.L. Jacobsen and K.H. Ipsen, “Aquifer based carbon dioxide disposal in

Denmark,” Energy Convers. Mgmt., 34:9-11, pp. 933-940, 1993.

20 Allinson, G. and V. Nguyen, “The economics of CO2 sequestration in Australia,” presented 

at the Fifth International Conference on Greenhouse Gas Control Technologies, Cairns, 

Australia, 2000.

21 Steefel, C., “Intercomparison of simulation models for CO2 disposal in underground storage 

reservoirs – Test problem 7: CO2 injection into a 2-D layered brine formation,” [Online 

document], [cited Apr. 2001], Available HTTP:

http://esd.lbl.gov/GEOSEQ/code/testprob_7.html 

22 Nguyen, Victor, personal communication, Jul. 17, 2001.

8-1 

8 

OCEAN VIA PIPELINE 

8.1 Introduction 

This chapter looks at the injection of CO2 into the deep ocean via a pipeline laid on the seabed. 

8.2 State of the Art 

The direct injection of CO2 into the ocean requires starting with a fairly concentrated stream of 

CO2 and delivering it to locations in the ocean where it will be effectively sequestered for 

hundreds of years, if not longer. To accomplish this, the CO2 needs to be injected below the 

thermocline at depths greater than 1000 meters.1 The CO2 may be injected at moderate depths of 

1,000 to 2,000 m or at depths greater than 3,000 m. The CO2 can be sequestered either by 

dissolution in the water column or by the formation of CO2 hydrates, which are solid, ice-like 

compounds. 

Led by offshore exploration and production activities of the oil and gas industry, great strides 

have been made in the development of undersea offshore technology. It is becoming routine for 

work to be done at depths approaching 2,000 m (6,600 feet). Work at much greater depths, even 

approaching 10,000 m, is possible at reduced scales and/or time horizons, as has been shown in 

deep drilling and other scientific programs. However, there are still many technical challenges in 

going deep at large scales for extended times. Therefore, as a first step, it appears that the best 

strategy is to discharge the CO2 below the thermocline at moderate depths of 1,000 to 2,000 m.2

The technology to proceed with this option is available. There is however a lack of information 

regarding how to adequately optimize the costs, determine the effectiveness of the sequestration 

and understand the resulting changes in the biogeochemical cycles of the oceans. This storage 

option is also limited by the fact that it is best suited to large, stationary CO2 sources with access 

to deep-sea sequestration sites — sources that may account for only about 15 to 20 percent of our 

anthropogenic CO2 emissions. 

Figure 8-1 illustrates that about 18 percent of worldwide power plant emissions are within a 400 

km offshore distance of 1,500 m water depth. Given that power plant CO2 emissions account 

for about 35 percent of total anthropogenic CO2 emissions, about 6 to 6.5 percent of CO2

emissions could be sequestered in the deep ocean from coastal power plants. 
Ocean Via Pipeline

8-2

Cumulative Distribution of Population and CO2 Emissions

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 500 1000 1500 2000 2500

Distance from 1,500 meter ocean depth (km)

Cumulative fraction

Population

Power Plant CO2 Emissions

Figure 8-1

Cumulative distribution of population and power plant CO2 emissions

8.3 Process Description

To make a more realistic case, it is assumed that three IGCC power plants supply CO2 to a single 

ocean pipeline. Based on this, the ocean pipeline system needs to be designed to handle three 

times the quantity of CO2, i.e. 11.29 million scm (22,167 tonnes), as opposed to 3.76 million scm 

(7,389 tonnes) of CO2 per day. At the shoreline collection point, additional compression is 

needed to bring the CO2 up to the ocean pipeline’s required inlet pressure of 152 bar. As a final 

note, it is assumed that this facility, like the power plant, has a lifetime and design/construction 

time of 20 and 4 years, respectively.

8.4 Methodology Used

The ocean pipeline storage option involves transporting the CO2 via a subsea pipeline from the 

shoreline to a depth of 2,000 m, at which depth the CO2 is discharged into the deep ocean via a 

diffuser unit. An offshore distance of 100 km, for the base case, is considered. The method 

used for calculating the cost of this process can be broken down into a couple of steps. First, the 

diameter of the subsea pipeline is determined. It is then possible, as a second step, to calculate 

the capital and O&M costs as well as the cost per tonne of CO2. These two steps are explained 

in greater detail below. Figure 8-2 gives an overview of the ocean pipeline cost model.
Ocean Via Pipeline

8-3

Figure 8-2

Ocean pipeline cost model overview diagram

8.4.1 Diameter Calculation

The pipeline diameter is calculated using the same method, described in Section 4.4.1, as is used 

in the CO2 overland pipeline transport model. The only difference is the means by which the 

maximum allowable pressure drop per unit length (?P/?L) is determined. In the case of CO2

overland pipeline transport, the pressure drop per unit length is simply found as the difference 

between the pipeline CO2 inlet and outlet pressures divided by the pipeline length. The pipeline 

ocean CO2 storage model however requires that the pressure drop per unit length calculation also 

take into account the gravity head gain and diffuser head loss. In addition, it is necessary that the 

CO2 be discharged at a pressure equal to the hydrostatic pressure.

The pipeline CO2 inlet pressure for the subsea pipeline is set at 152 bar, the same as the outlet 

value in the land-based cases. Given that the CO2 outlet pressure for the overland pipelines is set 

at 103 bar, this requires the use of booster pumps. The required pipeline outlet pressure, taken to 

be equal to the hydrostatic pressure of water at a depth of 2,000 m, is calculated to be 

approximately 200 bar. A diffuser head loss of 20 bar is then assumed. Next, calculating the 

average value of CO2 specific gravity over both the 0 to 1,000 m-depth and 1,000 to 2,000 mdepth intervals, and adding the respective CO2 pressure head gains, gives a gravity head of 194 

bar. Based on the set inlet pressure, assumed diffuser head loss and the calculated values of 

outlet pressure and gravity head, maximum allowable pressure drops per unit length for the base 

and sensitivity cases are found. Finally, the equations for pressure drop and head loss due to 

frictional resistance in a pipe, assuming turbulent flow, are used to determine the respective 

diameters. Figure 8-3 gives the calculated pipe diameter, for the base case, as a function of CO2

mass flow rate.

Inputs:

CO2 mass flow rate

Pipeline length

Injection depth

CO2 inlet pressure

Diffuser head loss

Capital charge rate

Outputs:

Total capital cost

Total O&M cost

Total annual cost 

Total cost per tonne CO2

OCEAN PIPELINE MODEL

Internal Calcs:

CO2 outlet pressure

Gravity head

Pressure drop per unit length

Pipe diameter

Capital costs:

Pipeline, injector unit, boost compressor

O&M costs:

Subsea maintenance, boost compressor 

power & maintenance
Ocean Via Pipeline

8-4

Diameter as a Function of CO2 

Mass Flow Rate

0

5

10

15

20

25

30

35

40

0 20 40 60

Mass flow rate (Mt CO2/yr)

Pipe diameter (in)

1 inch = 0.254 meters

Figure 8-3

Diameter for the base case as a function of CO2 mass flow rate

8.4.2 Cost Calculations

The cost of the subsea pipeline has been determined based on cost information contained in 

McDermott’s phase II final report on ‘Large-scale CO2 Transportation and Deep Ocean 

Sequestration. 3 Based on McDermott’s total capital cost of $3,224.5 million for six 30-inch, 

500-km long pipelines, a capital cost factor of $35,749/in/km ($57,659/in/mi) is calculated. The 

total annual O&M cost for the six pipelines was found by McDermott to be $75,400,000, where 

this excludes the cost of pump operation. 4 Based on this figure, an O&M cost factor of 

$25,078/yr/km ($40,448/yr/mi) is calculated. The capital cost of an injector unit, based on an 

estimate given in an IEA report,5 is taken to be $14.5 million.

The capital and yearly maintenance costs of the boost compressor are estimated at $9,355,000 

and $432,000, respectively. The cost of the power used by the compressor is taken to be $0.055 

per kilowatt-hour. These costs together with the costs associated with the subsea pipeline and 

injector unit are summarized in Table 8-1.
Ocean Via Pipeline

8-5

Table 8-1

Capital and O&M cost estimation factors

Parameter Unit Value

CAPITAL COSTS

Subsea Pipeline $/in/km 35,749

Injector Unit $ 14,500,000

Boost Compressor $ 9,355,000

O&M COSTS

Subsea Maintenance $/y/km 25,078

Boost Compressor Power $/kWh 0.055

Boost Compressor 

Maintenance

$ 432,000

8.5 Design Basis

8.5.1 Pipeline Design

The methodology described in Section 8.4.1 was used to determine pressure drop per unit length 

and pipeline diameter for the base case. The design basis for ocean storage via pipeline is 

summarized in Table 8-2.

Table 8-2

Design basis for ocean storage via pipeline

Parameter Unit Ocean Pipeline 

Base Case

Subsea Pipeline Length km 100

Injection Depth m 2,000

CO2 Inlet Pressure MPa 15.2

CO2 Outlet Pressure* MPa 20.0

Gravity Head* MPa 19.4

Diffuser Head Loss* MPa 2.0

Pressure Drop per Unit Length* Pa/m 126

Pipe Diameter* inches 14.2

Nominal Pipe Size* inches 16

* calculated

1 inch = 0.0254 meters
Ocean Via Pipeline

8-6

The calculated nominal pipe size for the base case is 16 inches (0.41 meters). This pipe diameter 

is larger than the 12-inch diameter (0.30 meters) for the case of CO2 overland pipeline transport. 

This is despite the value of maximum allowable pressure drop per unit length also being larger. 

This can be explained by the fact that the design CO2 mass flow rate used here is 11.29 million 

scm (22,167 tonnes) per day as opposed to 3.76 million scm (7,389 tonnes) per day.

8.5.2 Capital and O&M Cost Inputs

The capital and O&M costs of ocean storage via pipeline for the base case are calculated using 

the methodology described in Section 8.4.2. The results are shown in Table 8-3.

Table 8-3

Capital and O&M cost inputs for the ocean pipeline base case

Parameter Unit Ocean Pipeline 

Base Case

Subsea Pipe Diameter inches 14.2

Subsea Pipeline $ 50,900,000

Injector Unit $ 14,500,000

Boost Compressor $ 9,355,000

Subtotal $ 74,755,000

Subsea Maintenance $ 2,507,776

Boost Compressor

Power (5,650 kW)

$ 2,726,000

Boost Compressor 

Maintenance

$ 432,000

Subtotal $ 5,665,776

1 inch = 0.0254 meters

8.6 Model Results

This section presents costs for CO2 capture and storage for the base case described in Section 

8.5. The storage costs comprise transaction, transportation, injection and monitoring costs. 

These costs are combined with capture costs from an IGCC power plant. The results are given 

on several bases as described in Chapter 2. Appendix B provides a detailed description of each 

of the spreadsheets.

The Process Input sheet for the ocean pipeline base case is presented in Table 8-4.
Ocean Via Pipeline

8-7

Table 8-4

Process input sheet for the ocean pipeline base case

Design Basis Economic

IGCC Plant IGCC Plant (Case 3a)

Capacity Factor 80% CO2 Capture Cost ($/tonne captured) $14.55

Land Pipeline Distance 100 km COE Including CO2 Capture Cost ($/MWh) $55.08

Land Pipeline Distance 62 miles

Nominal pipe size (in) 12 IGCC Plant & CO 2

 Storage

After-Tax Discount Rate 6.09%

CO 2 Storage Levelized Carrying Charge Factor 15.0%

Injection Depth (m) 2000

Ocean Pipeline Distance 100 km

Ocean Pipeline Distance 62 miles

Nominal ocean pipe size (in) 16

Ocean Pipeline Storage Input Variables

1 inch = 0.0254 meters
Ocean Via Pipeline

8-8

The Summary sheet for the ocean pipeline base case is presented in Table 8-5. 

Table 8-5

Summary sheet for the ocean pipeline base case

Process Description Capital & O&M Cost

Land Pileline Distance 100 km CO2 Stored (M tonne/yr) 6.473 Capital O&M

Injection Depth (m) 2,000 CO2 Avoided (M tonne/yr) 5.464 Transaction $6,470,000 $0

Ocean Pipeline Distance 100 km LC Avoided GHG (M tonne/yr) 6.460 Transport $80,200,000 $3,999,000

LC Avoided GHG (M tonne/yr) 5.409 Sequestration $74,755,000 $5,665,776

Monitoring $0 $1,942,000

Economic Description

After-Tax Discount Rate 6.09% Total $161,430,000 $11,610,000

1. Levelized Annual CO2 Storage Costs (years 1-20) CO2 Captured Basis LC Avoided GHG Basis

(storage only)

CO2 Storage Cost, M $/yr 35.75 35.75

CO2 Stored or Avoided, M tonne/yr 6.47 6.46

Storage Cost, $/tonne CO2 5.52 5.53

2. Levelized Annual CO2 Capture and Storage Costs (years 1-20) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

CO2 Capture Cost, M $/yr 94.17 94.17 94.17

CO2 Storage Cost, M $/yr 35.75 35.75 35.75

Capture + Storage Cost, M$/yr 129.92 129.92 129.92

CO2 Stored or Avoided, M tonne/yr 6.47 5.46 5.41

Capture + Storage Cost, $/tonne CO 2 20.07 23.78 24.02

3. CO2 Capture and Storage Costs, NPV Basis (years 1-100) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

NPV of CO2 Capture Costs, M$ 1,072.27 1,072.27 1,072.27

NPV of CO2 Storage Costs, M$ 416.75 416.75 416.75

NPV of Capture + Storage Costs, M$ 1,489.02 1,489.02 1,489.02

NPV of CO2 Stored or Avoided, M tonne 73.70 62.21 61.52

Capture + Storage Cost (NPV Basis), $/tonne CO2 20.20 23.94 24.21

4. Cost of Electricity, $/MWh

Reference Plant (no capture) 43.98

Capture Costs (years 1-20) 11.10

Storage Costs (years 1-20) 4.21

Storage Costs, Monitoring, etc. (years 21-100) 0.10

Total Cost of Electricity 59.40

Ocean Pipeline Storage Summary

Cost of CO2 Capture & Storage

8.7 Sensitivity Analysis

The sensitivity of the CO2 storage cost for the ocean pipeline option is determined for offshore 

and pipeline distance. It can be seen in Figure 8-4 that an increase in both offshore and pipeline 

distance increases the storage cost. The storage cost is more sensitive to pipeline distance than 

offshore distance due to the fact that the ocean pipeline cost includes a fixed injector unit cost.
Ocean Via Pipeline

8-9

Ocean Pipeline Cost Sensitivity

Levelized Annual CO2 Storage Costs (LC Avoided GHG Basis)

4.00

4.50

5.00

5.50

6.00

6.50

7.00

-50% -30% -10% 10% 30% 50%

% Change in Variable

$/tonne CO2 eq. LC GHG avoided

Offshore Distance

Pipeline Distance

Figure 8-4

Sensitivity analysis for ocean pipeline

High and low cost cases have been chosen for the ocean pipeline option, and are presented 

together with the base case in Table 8-6.

Table 8-6

Ocean pipeline base, high cost and low cost cases

Parameter Units Ocean Pipeline

Base Case

Ocean Pipeline

High Cost Case

Ocean Pipeline

Low Cost Case

Pipeline Distance km 100 300 +200% 0 -100%

Offshore Distance km 100 300 +200% 50 -50%

The results for the high and low cost cases as well as the base case are given in Table 8-7. The 

CO2 storage cost for the ocean pipeline option differ by about a factor of 5 for the high and low 

cost cases.
Ocean Via Pipeline

8-10

Table 8-7

Results for ocean pipeline base, high cost and low cost cases

Parameter Units Ocean Pipeline

Base Case

Ocean Pipeline

High Cost Case

Ocean Pipeline

Low Cost Case

Pressure Drop per 

Unit Length

Pa/m 126 42 251

Pipe Diameter inches 14.2 17.5 12.4

Nominal Pipe Size inches 16 20 14

Levelized Annual 

CO2 Storage Cost

$/tonne CO2

Eq. LC GHG 

avoided

5.53 14.23 2.90

1 inch = 0.0254 meters

8.8 Comparison to Literature

8.8.1 Studies Used in Model Evaluation

The studies given in Table 8-8 all contain design and cost information for the transport of CO2

by subsea pipeline. In all cases, except the ‘GEODISC’ study, the pipeline is to be used for the 

purpose of injecting CO2 into the ocean. ‘GEODISC’ looks at subsea pipeline CO2 transport in 

the context of storing the gas in an offshore aquifer. 

Table 8-8

Subsea pipelines’ characteristics

Study CO2 flow 

rate (Mt/y)

Initial CO2

pressure 

(bar)

Diamete

r (m)

Lengt

h (km)

Injection 

depth 

(m)

Recompressi

on station 

included

IEA Ocean619.00 74 (liquid 

CO2)

0.800 100 500 Ignored

British Coal73.63 136 0.350 517 2,000 Yes

GEODISC85.67 205 0.660 200 100 No

McDermott9200 (total)

33.3 (each)

130 0.760 (6 

pipes)

500 3,000 Yes

UMass10 8.20 140 0.600 200 1,000 No

It should be noted that in the ‘McDermott’ study there is a large quantity of CO2, taken to be the 

emissions from forty 500 MWe coal-burning power stations, being injected into the ocean. This 

large quantity of CO2, 200 Mt/y, is determined in the study to require the use of one 
Ocean Via Pipeline

8-11

1.63-m-diameter pipe or six 0.760-m-diameter pipes. For purposes of making a diameter 

comparison with the model, taking into account its inability to allow for bundling, one 0.760 m 

diameter pipe carrying 33.3 Mt/yr of CO2 is assumed. It should be noted that a cost comparison 

is not made with the ‘McDermott’ study due to the fact that this study was used as a basis for 

costing.

The ‘GEODISC’ and ‘IEA ocean’ studies require the installation of a subsea pipeline up to a 

maximum depth of 100 and 500 m, respectively. The installation of these pipelines, at these 

relatively shallow depths, requires only the use of a ‘S-lay’ barge and does, as such, lie within 

the capabilities of existing technology. However, in the case of the ‘UMass’, ‘British Coal,’ and 

‘McDermott’ studies, which require pipeline installation at depths of 1000, 2000, and 3000 m, 

respectively, a combination of ‘S-lay’ and ‘J-lay’ techniques is needed. In addition, in the case 

of ‘British Coal’, as for ‘McDermott,’ modifications to the existing ‘J-lay’ barge would be 

necessary. As this upgrade would only be a one-time cost, it is not included in the cost analysis.

The ‘British Coal’ study, like ‘McDermott’, requires a shore-based pumping station to transport 

the CO2 a long distance. The cost of this pumping station is not included in the total cost of the 

scheme, calculated for the purpose of comparing the study value with the model output. The 

requirement of a pumping station in the ‘IEA Ocean’ study is ignored as this study deals with 

liquid, not supercritical, CO2.

8.8.2 Comparison of Values from Model and Studies

The graph below in Figure 8-4 shows pipe diameter as a function of CO2 mass flow rate. Also 

shown in the figure is the value of pipe diameter given in each of the five studies. 

Diameter as a Function of CO2 Mass Flow Rate

0

5

10

15

20

25

30

35

40

0 10 20 30 40 50 60

Mass flow rate (Mt CO2/yr)

Pipe diameter (in)

Ocean Pipeline Model (Base Case)

IEA ocean study

British Coal

GEODISC

McDermott

UMass
Ocean Via Pipeline

8-12

Figure 8-4

Comparison of pipe diameter values

Figure 8-5 shows the capital cost of the subsea pipeline as calculated by the model, where this 

excludes the cost of the boost compressor and transaction costs, as a function of CO2 mass flow 

rate. As for pipe diameter, the capital cost values given in the studies are reasonably close to 

those calculated by the model considering the uncertainties in the data.

Construction or Capital Cost

0

20

40

60

80

100

120

140

160

0 10 20 30 40 50 60

Mass flow rate (Mt CO2/yr)

Cost ($M/100 km)

Ocean Pipeline Model (Base Case)

IEA ocean study

British Coal

GEODISC

UMass

Figure 8-5

Comparison of subsea pipeline capital cost values
Ocean Via Pipeline

8-13

8.9 References

1 Herzog, H., ”Ocean Sequestration of CO2: An Overview,” presented at the Fourth

International Conference on Greenhouse Gas Control Technologies, Interlaken, Switzerland, 

August 30-September 2, 1998.

2 “Carbon Sequestration Research and Development,” Chapter 3, Ocean Sequestration,

http://www.fe.doe.gov/coal_power/sequestration/reports/rd/chap5.pdf

3 Sarv, H., “Large-scale CO2 transportation and deep ocean sequestration – Phase II final 

report,” McDermott Technology Inc., Ohio. Tech. Rep. DE-AC26-98FT40412, 2001.

4 Sarv, H., e-mail communication, Jan. 14, 2002.

5 Ormerod, B., “The disposal of carbon dioxide from fossil fuel fired power stations,” IEA 

Greenhouse R&D Programme, Cheltenham. Tech. Rep. IEAGHG/SR3, Jun. 1994.

6 Ormerod, W., “The disposal of carbon dioxide from fossil fuel fired power stations,” IEA 

Greenhouse R&D Programme, Cheltenham. Tech. Rep. IEAGHG/SR3, Jun. 1994.

7 Summerfield, I.R., S.H. Goldthorpe, N. Williams and A. Sheikh, “Costs of CO2 disposal 

options,” Energy Convers. Mgmt, vol. 34, no. 9-11, pp. 1105-1112, 1993.

8 Allinson, G. and V. Nguyen, “The economics of CO2 sequestration in Australia,” presented 

at Fifth International Conference on Greenhouse Gas Control Technologies Cairns,

Australia, 2000.

9 Sarv, H., “Large-scale CO2 transportation and deep ocean sequestration,” McDermott 

Technology Inc., Ohio. Tech. Rep. DE-AC26-98FT40412, Mar. 1999.

10 Golomb, D., “Transport systems for ocean disposal of CO2 and their environmental effects,” 

Energy Convers. Mgmt, vol. 38, suppl., pp. 279-286, 1997.

9-1

9

OCEAN VIA TANKER

9.1 Introduction

The ocean storage via tanker option involves transporting the CO2 by refrigerated tanker from a 

port facility to an offshore floating platform, where the CO2 is injected into the deep ocean 

through a vertical pipe.

9.2 State of the Art

To date, there have been no commercial or pilot-scale applications of this ocean storage option. 

The use of tankers to transport CO2 to an offshore floating platform is however within the current 

state of technology. All that is required is that existing floating platforms and so-called ‘semirefrigerated’ tankers that transport pressurized liquid chemicals, such as ammonia and liquid 

natural gas, be adapted for the purpose.1

Ocean storage via tanker would only be desirable where CO2 was to be injected into the deep 

ocean at a great distance, say 800 km, from the shoreline, in which case it would be more 

economical than the subsea pipeline option. It does also have the advantage that it would allow 

for easy relocation of the injection site.

9.3 Process Description

It is assumed, as in the case of ocean storage via pipeline, that three IGCC power plants supply 

CO2 to the shoreline collection point. Based on this, the ocean tanker system needs to be 

designed to handle three times the quantity of CO2, i.e., 11.29 million scm (22,167 tonnes), as 

opposed to 3.76 million scm (7,389 tonnes) of CO2 per day. As a final note, it is assumed that 

this facility, like the power plant, has a lifetime and design/construction time of 20 and 4 years, 

respectively.

9.4 Methodology Used

The storage option has been modeled based on design and cost information obtained from 

McDermott’s Phase I and Phase II final reports on ‘Large-scale CO2 Transportation and Deep 

Ocean Sequestration’1,2 as well as e-mail communications with the reports’ author.3,4 The 

method used for a conceptual design of this process can be broken down into a number of steps. 

First, the number of tankers required to transport the CO2 to the offshore platform is determined. 

Second, the diameter of the vertical pipe to carry the CO2 from the platform to the injection 
Ocean Via Tanker

9-2

depth is calculated. Third, the amount of CO2 emitted by the tankers traveling to and from the 

offshore storage site, and emitted due to boil off, is found. It is then possible, as a final step, to 

calculate the capital cost of the tankers, port facility, offshore floating platform, and vertical pipe, 

and the non-fuel and fuel O&M costs as well as the cost per tonne of CO2. These steps are 

described in greater detail below. An overview of the ocean tanker cost model is given in 

Figure 9-1.

9.4.1 Tanker Number Calculation

The number of tankers required is determined based on the use of tankers able to carry 22,000 m3

of CO2 at 7 bar and minus 50oC. A tanker of this description is, according to McDermott, within 

the current state of shipbuilding technology. The aforementioned temperature and pressure 

properties of CO2 give a CO2 density of 1155 kg per m3 and a tanker capacity, in terms of 

weight, of 25,410 tonnes of CO2. Given this latter tanker CO2 capacity, it is found from the 

design CO2 flow rate of 22,167 tonnes per day that the loading time for each tanker will be 

approximately 27.5 hours. The unloading time, which unlike the loading time is not dependent 

on the CO2 flow rate from the plant, is taken to be about six hours. Assuming a tanker speed of 

33 km per hour, it is then possible to calculate the time taken for a round trip and the amount of 

time for which a tanker is not at the CO2 collection point. Finally, this calculated value of time 

not at the collection point enables the number of tankers required to be en route at any given time 

to be determined. For the base case, it is found that two tankers would be needed. An additional 

tanker is, however, added, bringing the total number required to three, to allow for tanker 

downtime.

9.4.2 Vertical Pipe Diameter Calculation

The same method as is used for pipe sizing in the subsea pipeline ocean CO2 storage model, 

described in Section 8.4.1, is used to calculate the diameter of the vertical pipe. The required 

pipe outlet pressure, taken to be equal to the hydrostatic pressure of water at a depth of 2,000 m, 

is calculated to be approximately 200 bar. Next, calculating the average value of CO2 specific 

gravity over both the 0 to 1,000-m-depth and 1,000- to 2,000-m-depth intervals, and adding the 

respective CO2 pressure head gains, gives the gravity head. Based on the set inlet pressure of 

152 bar and these calculated values of outlet pressure and gravity head, a maximum allowable 

pressure drop per unit length of 7,281 Pa per m is then found. Finally, the diameter is 

determined from the equations for pressure drop and head loss due to frictional resistance in the 

pipe, assuming turbulent flow. For the base case, a nominal 8-inch-diameter vertical pipe is 

required.
Ocean Via Tanker

9-3

TANKER NUMBER 

CALCULATION

Internal Calcs:

CO2 density

Tanker CO2capacity

Loading time

Unloading time

Time taken for round trip

Time not at collection point

Number of tankers

COST MODEL

Internal Calcs:

Capital costs:

Tankers, offshore floating 

platform, onshore port facility, 

vertical pipeline, general 

facilities etc.

O&M costs:

Non-fuel, fuel

Inputs:

Diesel fuel price

Capital charge 

rate

Outputs:

Total capital cost

Total O&M cost

Total annual cost 

Total cost per tonne

CO2

PIPELINE DIAMETER 

CALCULATION

Internal Calcs:

CO2 density

CO2viscosity

CO2 outlet pressure

Gravity head

Pressure drop per unit 

length

Vertical pipeline diameter

TANKER CO2 EMISSIONS 

CALCULATION

Internal Calcs:

Number of round trips

Total annual distance 

traveled

Total annual fuel usage

Total annual CO2 emitted 

Inputs:

CO2 mass flow rate

Tanker volume

Tanker pressure

Tanker temperature

Tanker speed

Offshore distance

Inputs:

CO2 mass flow rate

CO2inlet pressure

CO2temperature

Injection depth

Inputs:

Tanker fuel usage

Diesel energy content

Diesel CO2 emissions factor

Offshore distance

BOIL OFF CO2

EMISSIONS 

CALCULATION

Internal Calcs:

Total annual CO2 emitted

Inputs:

CO2 mass flow rate

Boil off

Time taken for round trip

Number of tankers required

Pipe diameter

Annual CO2 emitted 

bbooff

Annual fuel usage

Annual CO2emitted

Figure 9-1

Ocean tanker cost model overview diagram
Ocean Via Tanker

9-4

9.4.3 CO2 Emissions Calculations

The quantity of CO2 to be emitted by the tankers needs to be determined. This requires that the 

quantity of fuel used by a 22,000 m3-tanker, in terms of gallons per km, first be calculated. In 

order to do this, it was necessary to refer to the small tanker case study given in the McDermott 

reports. In this case study, the total capital cost of the required 38 22,000-m

3

 tankers is $2,100 

million. According to Hamid Sarv of McDermott, the annual tanker O&M cost was estimated as 

being equal to 5.6 percent of the tanker capital cost, of which the tanker fuel cost comprised 16.5 

percent. Based on these percentages, the tanker fuel cost is equal to $19.4 million per year. 

Given that the price of diesel fuel is about $0.566 per gallon4, the total annual quantity of fuel 

used by the tankers is found to be approximately 34.3 million gallons. The corresponding total 

annual distance traveled by the tankers is calculated to be 8.76 million km. Dividing the total 

annual distance traveled by the total annual fuel used, gives a tanker fuel usage of 3.91 gallons 

per km.

Given this calculated value of tanker fuel usage, it is possible to determine the CO2 emitted by 

the tankers. Diesel fuel has an energy content of around 137 million joule per gallon and a CO2

emissions factor of close to 70 milligrams per joule. Given that the fuel used by a tanker is 3.91 

gallons per km, the amount of CO2 emitted by a tanker per km traveled can be found to be 

37,614 grams. The total annual distance traveled is calculated by multiplying the total number of 

round trips per year, equal to the number of hours in a year divided by the loading time of 27.5 

hours, by the respective round-trip distance. The total distance traveled by the tankers is found 

to be 63,683 km. Multiplying the amount of CO2 emitted per km by the total annual distance 

traveled gives a total annual amount of CO2 emitted of 2,395 tonnes.

The amount of CO2 emitted due to boil off is estimated as one percent per day of the amount of 

CO2 transported by tanker based on industry experience with CO2 truck tankers. It is important 

to note that the calculation assumes that the CO2 is in the tanker, and therefore undergoing boil 

off, for only one half the number of hours taken for a round trip. This CO2 quantity is then 

multiplied by 365 days to give the amount of CO2 emitted per year. The annual quantity of CO2

emitted is calculated to be 53,362 tonnes.

9.4.4 Cost Calculations

The total capital cost of the tanker ocean CO2 storage option comprises the capital cost of the 

three required tankers, the offshore floating platform, the port facility, and a 2,000-m long, 8-

inch diameter (0.20 meter) vertical pipe. The capital cost of the tankers is found using 

McDermott’s cost estimate of $55.3 million for a single 22,000-m

3

 tanker. For the offshore 

floating platform, the capital cost of $200 million also given in the McDermott report is used. In 

the case of the port facility, for which no cost estimate was provided, a capital cost of 

$50 million is assumed. Next, based on cost data in the report, the capital cost of the vertical 

pipe is calculated. The vertical pipe’s capital cost is taken to include $351,445/in/km 

($566,847/in/mi) for pipe marshalling and the attaching of buoys and corrosion anodes, a 

$0.3 million cost for towing the pipe to the offshore structure and a $3 million cost for pipe 

upending, securing, and anchoring. Finally, a 30 percent surcharge is added to all capital 
Ocean Via Tanker

9-5

expenses to cover costs associated with general facilities, engineering, permitting, and 

contingencies.

The total O&M cost is calculated as the sum of the non-fuel and fuel O&M costs. From e-mail 

communications with Hamid Sarv3,4, it was learned that the total annual O&M cost in the case 

studies was estimated as the sum of 5.6 percent and 0.02 percent of the total tanker and 

non-tanker capital costs, respectively, where the fuel cost comprised 16.5 percent of the tanker 

O&M cost. The non-fuel O&M cost is calculated in the model as 4.7 percent of the total tanker 

capital cost, thus excluding the fuel cost, plus 0.02 percent of the total non-tanker capital costs. 

The fuel O&M cost is determined as the product of the total annual fuel usage, found from 

multiplying the tanker fuel usage by the total annual distance traveled, and a diesel fuel price of 

$0.566 per gallon.

The capital and O&M cost estimation factors are summarized in Table 9-1.

Table 9-1

Capital and O&M cost estimation factors

Parameter Unit Value

CAPITAL COSTS

Tanker $/tanker 55,263,000

Offshore Platform $ 200,000,000

Onshore Port Facility $ 50,000,000

Vertical Pipeline:

Construction $/in/km 351,445

Towing to Offshore Structure $ 300,000

Upending, Securing & Anchoring $ 3,000,000

General Facilities, 

Engineering, Permitting etc.

$ 0.3*(Tanker_capital_cost + 

Offshore_platform_capital_cost + 

Onshore_port_facility_capital_cost + 

Vertical_pipeline_capital_cost)

O&M COSTS

Non-fuel $/yr (Tanker_capital_cost*0.047) + 

((Offshore_platform_capital_cost + 

Onshore_port_facility_capital_cost + 

Vertical_pipeline_capital_cost)*0.02) 

Fuel $/gal 0.566

1 inch = 0.0254 meters
Ocean Via Tanker

9-6

9.5 Design Basis

9.5.1 System Design 

The methodology described in Section 9.4.1 through Section 9.4.3 was used to calculate the 

required number of tankers, the diameter of the vertical pipeline, and the CO2 emissions from the 

tankers and due to boil off. Table 9-2 shows the results.

Table 9-2

Design basis for ocean storage via tanker

Parameter Unit Ocean Tanker 

Base Case

Offshore Distance km 100

Injection Depth m 2,000

Tanker Volume m

3

22,000

Tanker Pressure bar 7

Tanker Temperature deg C -50

Tanker CO2 Capacity kg/m325,410

Loading Time h 27.5

Unloading Time h 6

Tanker Speed km/h 33

Time Taken for Round Trip h 39.6

Number of Tankers 3

Vertical Pipe Inlet Pressure MPa 15.2

Vertical Pipe Outlet Pressure MPa 20.0

Gravity Head MPa 19.4

Pressure Drop per Unit 

Length

Pa/m 7,281

Vertical Pipe Diameter inches 6.5

Nominal Vertical Pipe Size inches 8

Tanker Fuel Usage gal/km 3.91

Diesel Energy Content million joule/gal 137

Diesel CO2 Emissions Factor mg/joule 70

Total Annual Distance 

Traveled

km/y 63,683

Total Annual Fuel Usage gal/y 249,001

CO2 Emitted by Tankers t/y 2,395

Boil Off %/d 1

CO2 Emitted by Boil Off t/y 53,362

1 inch = 0.0254 meters, 1 gal = 3.79 liters
Ocean Via Tanker

9-7

9.5.2 Capital and O&M Cost Inputs

The capital and O&M costs of ocean storage via tanker for the base case were calculated using 

the methodology described in Section 9.4.4. The results are shown in Table 9-3.

Table 9-3

Capital and O&M cost inputs for the ocean tanker base case

Parameter Unit Ocean Tanker 

Base Case

Number of Tankers 3

Vertical Pipeline Diameter inches 6.5

Total Annual Fuel Usage gal/y 249,001

CAPITAL COSTS

Tanker $ 166,000,000

Offshore Floating Platform $ 200,000,000

Onshore Port Facility $ 50,000,000

Vertical Pipeline:

Construction $ 4,580,000

Towing to Offshore Structure $ 300,000

Upending, Securing & 

Anchoring

$ 3,000,000

General Facilities, 

Engineering, Permitting etc.

$ 127,000,000

Subtotal $ 550,880,000

O&M COSTS

Non-fuel $ 12,900,000

Fuel $ 140,935

Subtotal $ 13,040,935

1 inch = 0.0254 meters

9.6 Model Results

This section presents costs for CO2 capture and storage for the base case described in 

Section 9.5. The storage costs comprise transaction, transportation, injection and monitoring 

costs. These costs are combined with capture costs from an IGCC power plant. The results are 

given on several bases as described in Chapter 2. Appendix B provides a detailed description of 

these spreadsheets.
Ocean Via Tanker

9-8

The Process Input sheet for the ocean tanker base case is presented in Table 9-4.

Table 9-4

Process input sheet for the ocean tanker base case

Design Basis Economic

IGCC Plant IGCC Plant (Case 3a)

Capacity Factor 80% CO2 Capture Cost ($/tonne captured) $14.55

Pipeline Distance 100 km COE Including CO2 Capture Cost ($/MWh) $55.08

Pipeline Distance 62 miles

Nominal pipe size (in) 12 IGCC Plant & CO 2

 Storage

After-Tax Discount Rate 6.09%

CO 2 Storage Levelized Carrying Charge Factor 15.0%

Injection Depth (m) 2000

Offshore Distance 100 km

Offshore Distance 62 miles

Tanker CO2 boil off rate (%/day) 1.0%

Nominal vertical pipe size (in) 8

Number of tankers needed 3

Ocean Tanker Storage Input Variables
Ocean Via Tanker

9-9

The Summary sheet for the ocean tanker base case is presented in Table 9-5.

Table 9-5

Summary sheet for the ocean tanker base case

Process Description Capital & O&M Cost

Pileline Distance 100 km CO2 Stored (M tonne/yr) 6.473 Capital O&M

Injection Depth (m) 2,000 CO2 Avoided (M tonne/yr) 5.411 Transaction $6,470,000 $0

Offshore Distance 100 km LC Avoided GHG (M tonne/yr) 6.408 Transport $80,200,000 $3,999,000

LC Avoided GHG (M tonne/yr) 5.358 Sequestration $550,880,000 $13,040,935

Monitoring $0 $647,000

Economic Description

After-Tax Discount Rate 6.09% Total $637,550,000 $17,690,000

1. Levelized Annual CO2 Storage Costs (years 1-20) CO2 Captured Basis LC Avoided GHG Basis

(storage only)

CO2 Storage Cost, M $/yr 113.03 113.03

CO2 Stored or Avoided, M tonne/yr 6.47 6.41

Storage Cost, $/tonne CO2 17.46 17.64

2. Levelized Annual CO2 Capture and Storage Costs (years 1-20) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

CO2 Capture Cost, M $/yr 94.17 94.17 94.17

CO2 Storage Cost, M $/yr 113.03 113.03 113.03

Capture + Storage Cost, M$/yr 207.20 207.20 207.20

CO2 Stored or Avoided, M tonne/yr 6.47 5.41 5.36

Capture + Storage Cost, $/tonne CO2 32.01 38.29 38.67

3. CO2 Capture and Storage Costs, NPV Basis (years 1-100) CO2 Captured Basis CO2 Avoided Basis LC Avoided GHG Basis

NPV of CO2 Capture Costs, M$ 1,072.27 1,072.27 1,072.27

NPV of CO2 Storage Costs, M$ 1,290.21 1,290.21 1,290.21

NPV of Capture + Storage Costs, M$ 2,362.48 2,362.48 2,362.48

NPV of CO2 Stored or Avoided, M tonne 73.70 61.61 60.93

Capture + Storage Cost (NPV Basis), $/tonne CO2 32.05 38.34 38.77

4. Cost of Electricity, $/MWh

Reference Plant (no capture) 43.98

Capture Costs (years 1-20) 11.10

Storage Costs (years 1-20) 13.32

Storage Costs, Monitoring, etc. (years 21-100) 0.03

Total Cost of Electricity 68.44

Ocean Tanker Storage Summary

Cost of CO2 Capture & Storage
Ocean Via Tanker

9-10

9.7 Sensitivity Analysis

The sensitivity of the CO2 storage cost for the ocean tanker option is determined for four key 

parameters: pipeline and offshore distance, boil off and diesel price. It can be seen in Figure 9-2

that increases in pipeline and offshore distance, boil off and diesel price all increase the storage 

cost. More noteworthy, the figure shows that changes in pipeline distance have the greatest 

effect on storage cost.

Ocean Tanker Cost Sensitivity

Levelized Annual CO2 Storage Costs (LC Avoided GHG Basis)

16.50

17.00

17.50

18.00

18.50

19.00

-50% -30% -10% 10% 30% 50%

% Change in Variable

$/tonne CO2 eq. LC GHG avoided

Offshore Distance

Pipeline Distance

Boil Off

Diesel Price

Figure 9-2

Sensitivity analysis for ocean tanker

High and low cost cases have been chosen for the depleted gas reservoir option, and are 

presented together with the base case in Table 9-6. For the high and low cost values for each of 

the four key parameters, the percentage change in the value from the base case is shown. This is 

done to illustrate the fact that the range in the values of some parameters is expected to be greater 

than for others.
Ocean Via Tanker

9-11

Table 9-6

Ocean tanker base, high cost and low cost cases

Parameter Units Ocean Tanker

Base Case

Ocean Tanker

High Cost Case

Ocean Tanker

Low Cost Case

Pipeline Distance km 100 300 +200% 0 -100%

Offshore Distance km 100 300 +200% 50 -50%

Boil Off %/day 1 2 +100% 0.5 -50%

Diesel Price $/gal 0.566 0.8 +41% 0.45 -20%

The results for the high and low cost cases as well as the base case are given in Table 9-7. The 

CO2 storage cost for the depleted gas reservoir option varies very little for the high and low cost 

cases.

Table 9-7

Results for ocean tanker base, high cost and low cost cases

Parameter Units Ocean Tanker

Base Case

Ocean Tanker

High Cost 

Case

Ocean Tanker

Low Cost 

Case

Number of Tankers 3 3 3

Total Annual Fuel Usage gal/yr 249,001 747,004 124,501

CO2 Emitted by Tankers t/yr 2,395 7,186 1,198

CO2 Emitted by Boil Off t/yr 53,362 139,415 24,638

Levelized Annual CO2

Storage Cost

$/tonne 

CO2 Eq. LC 

GHG 

avoided

17.64 22.79 15.76

9.8 Comparison to Literature

There is no cost data in the literature with which to make a comparison.
Ocean Via Tanker

9-12

9.9 References

1 Sarv, H., “Large-scale CO2 transportation and deep ocean sequestration,” McDermott 

Technology Inc., Ohio. Tech. Rep. DE-AC26-98FT40412, Mar. 1999.

2 Sarv, H., “Large-scale CO2 transportation and deep ocean sequestration – Phase II final 

report,” McDermott Technology Inc., Ohio. Tech. Rep. DE-AC26-98FT40412, 2001.

3 Sarv, H., e-mail communication, Feb. 4, 2002.

4 Sarv, H., e-mail communication, Feb. 20, 2002.
10-1

10

CO2 SINK ENHANCEMENT IN FORESTS—BASIS AND 

APPROACH

10.1 Introduction

From a utility perspective, the cost of GHG emission offsets ($/tonne C equivalent GHG 

avoided) from forestry sink enhancement options depends on the following: (1) levels of CO2

sink enhancement, (2) changes in GHG emissions other than from CO2 sink enhancement (e.g., 

from methane and nitrous oxide emissions), (3) reimbursement of forest management companies 

for net costs of changes in forest land area and/or management, (4) transaction costs for 

aggregating and brokering GHG credits, and (5) monitoring costs for assuring that contractual 

obligations are fulfilled. These aspects are addressed below.

The IEA Greenhouse Gas R&D Programme (IEA GHG), in consultation with the Edinburgh 

Centre for Carbon Management (ECCM), provided the following data on the economics of 

forestry CO2 sink enhancement options. Most of this information relates to developing 

countries, because that is where the greatest amount of forestry CO2 sink enhancement is likely 

to occur, but some information is also provided on carbon sequestration in U.S. forests.

10.2 Study Basis

10.2.1 Overall Basis

Some forestry carbon sink enhancement projects have already been implemented on a voluntary 

basis. A brief summary of the extent and costs of these projects is provided as background 

information in Appendix C, but the costs of these pioneer projects cannot be compared directly 

to the costs of other sink enhancement techniques because they have been derived using a variety 

of economic conventions and the price of carbon credits has been greatly affected by supply and 

demand. In this study, costs of sink enhancement are calculated from the quantities and timing 

of carbon stored and costs of forest establishment, maintenance, land, and timber revenue.

The performances and costs of forestry sequestration cover a very wide range, depending on 

local factors such as existing land uses, climate, soil conditions, the type of sequestration 

scheme, types of trees, land values and labor costs. It is not possible to cover all of these factors 

in a study such as this. Instead, information is provided for a diverse range of seven forestry 

sequestration cases, described in Section 10.3 and Chapters 11-17. These cases cover 

afforestation, reforestation, and avoidance of deforestation, developed and developing countries, 

and temperate and tropical climates.
CO2 Sink Enhancement in Forests—Basis and Approach

10-2

10.2.2 Carbon Sink Enhancement

When a forest grows, it removes CO2 from the atmosphere and sequesters carbon in trees and 

other above ground biomass, litter, humus, roots and soil. Eventually the forest reaches a 

dynamic equilibrium when the rate of new sequestration of carbon is equal to the rate of natural 

conversion of carbon back to CO2. The amount of carbon sequestered in forests can be increased 

by planting new forests (afforestation), restoring degraded forests (reforestation) or avoiding the 

destruction of existing forests (deforestation).

Carbon is sequestered in forests in above-ground biomass (stems and foliage) and in roots, litter 

on the forest floor, humus and soil. This study considers carbon stored in above-ground biomass, 

the most easy to verify, and carbon stored below ground.

Greenhouse gas emissions can also be reduced beyond the forest itself. Wood can be harvested 

sustainably from forests and converted into useful products such as timber for buildings, 

furniture, paper etc. Some of the carbon in these products will be quickly converted back to CO2

but some will remain sequestered as carbon for many decades. Carbon stored in wood products 

is included in this study.

Further information on carbon sequestration is given in Section 10.3.

10.2.3 Reduction in Emissions Resulting from Use of Wood and Biomass

Wood products can displace materials such as steel or concrete which generate significant 

amounts of CO2 during their production. Reductions in emissions of greenhouse gases from 

production of materials which are displaced by wood are beyond the scope of this study.

Wood and other forest biomass can be used as fuel, displacing fossil fuels. Production and use 

of biomass fuels is beyond the scope of this study.

10.2.4 Non-CO2 Greenhouse Gases

Forests and other land-uses emit significant quantities of non-CO2 greenhouse gases, particularly 

N2O. Emissions of N2O and CH4 are taken into account in this study but emissions of other nongreenhouse gases and particulates which can indirectly affect the climate are not. Further 

information on non-CO2 greenhouse gases is given in Section 10.3.

10.2.5 Ancillary Benefits

Forestry projects have many benefits in addition to those related to greenhouse gases. Natural 

forests, usually have high biodiversity, so preserving existing forests can be especially beneficial. 

Forest plantations can negatively affect biodiversity if they repla ce biologically rich native 

grasslands or wetland habitat but they can be designed to enhance biodiversity by stimulating 

restoration of natural forests.
CO2 Sink Enhancement in Forests—Basis and Approach

10-3

By reducing run-off, forests can conserve water resources and reduce the risk of flooding. They 

can also control erosion which can reduce siltation of rivers and protect fisheries and investment 

in hydro-electric power facilities.

Forests tend to have a higher landscape value than alternative land uses such as scrub and 

agricultural land and they have more potential for recreational activities. These benefits are 

likely to be greatest in areas which are accessible to large numbers of people and which currently 

have little variation in land form.

The overall philosophy of this study is that ancillary costs and benefits, such as those described 

previously, are not taken into account in the economic assessment. The reasons for this are:

• Attempting to quantify these costs and benefits involves a large amount of work that is 

beyond the scope of this study.

• The costs and benefits are very uncertain. They depend greatly on small scale local factors 

and the values of many of the benefits, for example preservation of biodiversity, are very 

subjective.

• The economic mechanisms do not usually exist to enable ancillary benefits or costs to accrue 

to the owners and operators of forest projects.

10.2.6 Concerns and Uncertainties

There are several concerns and uncertainties about forestry sequestration as a greenhouse gas 

abatement technique, including additionality, baselines, leakage, security and effects on the 

climate other than those due to greenhouse gases. To allow for these concerns, a tonne of carbon 

sequestered in forests is sometimes assumed to be not equivalent to a tonne of carbon emissions 

avoided by other means. It has been proposed that credit should be claimed for only a fraction of 

the carbon thought to be sequestered in forests, to allow for the concerns mentioned above. Such 

reductions in carbon credits are highly subjective and may be influenced by political factors, so 

they are not used in this study. However, when comparing costs and performances it should be 

recognized that international climate treaties, regulators and the public may regard a tonne of 

carbon sequestered in forests as not of equal value to a tonne of carbon emissions permanently 

avoided.

10.2.6.1 Additionality

Additionality essentially means “would the project have occurred anyway, regardless of carbon 

sequestration subsidies.” Large areas of forests are already being planted for commercial 

production of wood and also large areas of existing non-commercial forests are not being 

destroyed, despite the absence of carbon sequestration subsidies. Project developers need to 

demonstrate that their project would not have occurred without carbon sequestration credits. The 

probability that the project would have occurred anyway is greatest for projects that are 

economically attractive without carbon sequestration credits. However, just because a project is 

economically viable without carbon credits does not mean that it would have gone ahead 

anyway. There are many potential barriers; capital may not be available, risks may be too high, 
CO2 Sink Enhancement in Forests—Basis and Approach

10-4

infrastructure may be inadequate, legal or technological barriers may exist etc. Overcoming 

these barriers may enable a project to go ahead.

10.2.6.2 Leakage

In the context of forestry sequestration, leakage means the indirect impact that a targeted activity 

in a certain place at a certain time has on carbon storage at another place or time. Leakage in this 

sense should not be confused with physical leakage of CO2 from an underground or ocean 

storage reservoir due to geological faults, ocean currents etc.

Leakage can be induced through several different mechanisms, particularly activity shifting and 

demand displacement. Activity shifting tends to arise around the boundaries of the project and is 

related to socio-economic conditions of the surrounding population. For example, local people 

may be using land for agriculture and grazing purposes. If this land is then reforested or 

afforested, the local people may be forced to move to other areas where they will clear forest 

resulting in emissions that would not have arisen if the project were not implemented.

Market based leakage occurs when a forest protection or plantation project reduces the supply of 

a marketed product, either wood or agricultural products, resulting in increased felling of trees 

elsewhere to satisfy the demand for the product. This occurs in a diffuse way through global 

markets. Output from plantations that are subsidized for their carbon sequestration benefits can 

increase the supply, and hence reduce the price, of wood. This would tend to make forests that 

cannot claim carbon sequestration credits less economically attractive, so the amount of nonsubsidized plantations may decrease, resulting in less carbon sequestration. A study of the 

impacts on timber markets of 50 million ha of sequestration forests1 predicted that the impacts on 

the net quantity of carbon sequestered would be fairly small, about 10 percent. If forests were 

planted on agricultural land, this would tend to reduce the supply of food. The price of food 

would therefore increase, making conversion of forest elsewhere to agricultural land more 

attractive.

The only way to address leakage resulting from unfulfilled demand for wood or agricultural 

products is to invest in the supply chain elsewhere or reduce demand for products. For example, 

some of the revenue from a sequestration project could be invested in intensification of 

agriculture, to enable the same amount of food to be produced from a smaller area or land. This 

intensification of agriculture may affect greenhouse gas emissions, for example CO2 emitted 

during production of fertilizers and increased emissions of N2O from agricultural land. These 

effects are beyond the scope of this study.

10.2.6.3 Security

In common with most sequestration options, there is a potential for carbon stored in forests to be 

converted to CO2 and emitted to the atmosphere. Losses of carbon from forests could be 

classified as permanent or non-permanent. Non-permanent losses include forest fires and pest 

infestations. In most cases forests would re-grow naturally or they could be re-planted after such 

occurrences. There would be a temporary emission of CO2 but in the long term the carbon 
CO2 Sink Enhancement in Forests—Basis and Approach

10-5

sequestration at the site could be restored, although there would be an economic cost of reestablishing the forest.

Permanent loss of sequestration could occur due to social and political factors or climate change. 

Climate change could mean that some areas could become no longer suitable for forests because 

of changes in temperature and rainfall. For example, some global climate modelling has 

predicted that vast areas of tropical forests in northern Brazil will die back around the middle of 

this century because of anthropogenic climate change2. Temperate grasslands are also expected 

to expand into regions of Europe and North America that are currently dominated by temperate 

or coniferous forests. Areas suitable for forests are predicted to expand northwards in North 

America and Asia and there are even suggestions that climate change may enable forests to grow 

in the Sahara region3. It may therefore be possible to replace sequestration forests lost due to 

climate change by planting or encouraging the natural growth of forests in other regions but this 

would involve disruption and economic costs. It is very difficult to predict these risks of forest 

loss because of the large uncertainties in predictions of regional impacts of climate change.

Forests can be lost due to social factors, for example population growth leading to increased 

requirements for agricultural and urban land. It is important that forest projects are structured to 

provide benefits to local inhabitants to reduce the risks of forest loss. The legal status of forests 

in some countries, where land ownership or tenure is less secure, may result in an increased risk 

of forest loss.

10.2.6.4 Non-Greenhouse Gas Effects

Vegetation cover can have a significant impact on the albedo (reflectivity) of the Earth. Dark 

green forests tend to absorb more solar energy than crop lands. The greatest effect is during the 

winter months when large unforested areas are covered in highly reflective snow. In a 

coniferous forest the snow tends to fall to the ground and the dark surface of the treetops is 

exposed and absorbs more solar energy. Recent work using global climate models4 has indicated 

that in some parts of the world, including large parts of Canada and the Russian Far East, the 

extra solar energy absorbed would more than offset the greenhouse gas benefits of carbon 

sequestered by forests. Even in the US South, the albedo effect is predicted to offset around 10-

20 percent of the carbon sequestration benefits of forests. These estimates are subject to many 

uncertainties, notably the predictions of snow amount and surface reflectivity and they are based 

on the current climate. However, they do show that solar reflectivity could have a significant

impact on the climate change impacts of forests. Non-greenhouse has effects are beyond the 

scope of this study.

10.3 Sequestration Cases

10.3.1 Descriptions

The seven forestry sequestration cases assessed in this study are summarized in Table 10-1 and 

are described in detail in Chapters 11-17.
CO2 Sink Enhancement in Forests—Basis and Approach

10-6

Table 10-1

Summary of forestry sequestration cases

Case Type of management Type of trees Country/region

1 Plantation Loblolly pine USA (South)

2 Plantation Douglas fir USA (Pacific North-west)

3 Plantation Spanish Cedar Mexico

4 Restoration Pine-oak Mexico

5 Restoration Miombo Southern Africa

6 Agro-forestry Mango-tamarind India (South)

7 Avoidance of deforestation Various Mexico

In the case studies, descriptions of the ecology, economics and management of these forestry 

systems are provided and case 7 (avoided deforestation) contains a description of the 

methodology to be applied to derive factors to parameterize the deforestation model.

The information provided in the case studies provides an indication of the costs and 

performances of forestry sequestration projects but it should be recognized that the results 

depend on many local factors. Actual carbon uptake and economic performance of forestry 

systems will depend upon local factors, such as micro-climate, soil conditions and rainfall 

patterns that require on the ground assessment and monitoring of conditions. Individual site / 

project inspections will also be necessary to determine factors such as potential leakage of 

project benefits, what the baseline level of carbon storage is likely to be and whether the project 

implementers actually have the technical skill, equipment, legal rights, financial and 

management capabilities to implement a project, as proposed.

In developing countries the social context for forestry projects is extremely important. Forestry 

systems are but one of a number of components that form the basis for rural livelihoods.

10.3.2 Quantities of Carbon Sequestered

The quantity of carbon sequestered depends on the site productivity. For some of the cases, 

information on the quantities of carbon sequestered is provided for high, medium and low 

productivity sites. For other cases, because of limited available information, information is only 

provided for typical sites.
CO2 Sink Enhancement in Forests—Basis and Approach

10-7

10.3.2.1 Above-Ground Carbon

The quantities of carbon sequestered in above ground biomass in each of the cases, and how they 

vary with time, are summarized in Figure 10-1. This information is for medium productivity 

sites.

0.0

50.0

100.0

150.0

200.0

250.0

300.0

0 20 40 60 80 100

Year

Carbon sequestered (vegetation), t/ha

Loblolly

Fir

Cedar

Pine-Oak

Miombo

Mango

Figure 10-1

Quantities of carbon sequestered (medium productivity sites)

It can be seen that the quantities differ substantially between the cases, in terms of both carbon 

sequestration rate and the maximum quantity of carbon sequestered per hectare. The cases 

which give the fastest sequestration rates in early years do not necessarily sequester the largest 

quantities of carbon in the long term.

The quantities of carbon stored increase until the time when wood is harvested. If wood were 

not harvested the quantities of carbon stored may increase further but would then begin to 

decline, due to increased mortality. Harvesting wood and replanting has the benefit of producing 

revenue which offsets some of the costs of planting and maintaining the forests. Providing 

revenue and products for local use can be particularly important for developing countries, to 

maintain the support of local communities and reduce the risks of human induced forest loss.

The quantity of carbon sequestered depends on the site productivity. An example of this, for the 

Spanish Cedar plantations case, is shown in Figure 10-2. The quantities of carbon sequestered 

differ significantly between different site productivities but, in general the differences are less 

than between the different forest cases.
CO2 Sink Enhancement in Forests—Basis and Approach

10-8

0.0

50.0

100.0

150.0

200.0

0 20 40 60 80 100

Years

Carbon sequestered (vegetation), tC/ha

Low

Medium

High

Figure 10-2

Carbon sequestered in Spanish Cedar plantations at different site productivities

10.3.2.2 Below-Ground Carbon

It is relatively easy to monitor and verify carbon stored in above-ground trees. Carbon 

sequestration credits are often based only on above ground carbon. Carbon in soils can be 

measured but it is more uncertain and many more measurements are required. This study 

includes both above-ground and carbon stored in soils. The base case economic assessments 

include both above ground carbon and carbon in soil, roots.

10.3.2.3 Wood Products

Carbon stored in products made from harvested wood is also considered in this study. Some 

wood products, e.g., paper, have short lifetimes, but others, e.g., wood used in buildings, can 

have lives of up to 100 years or more. Carbon stored in wood products is sometimes included in 

assessments of carbon sequestration but there are many uncertainties and there is no simple way 

to verify the quantity of carbon stored.

The quantities of carbon sequestered in above ground vegetation, soil (including roots, litter and , 

humus) and timber products in the medium productivity Spanish Cedar case are shown in Figure 

10-3. The quantities of carbon shown in Figure 10-3 are in addition to those already existing at 

the start of the project. The figure illustrates the characteristic pattern of carbon stored in wood 

products.
CO2 Sink Enhancement in Forests—Basis and Approach

10-9

0.0

50.0

100.0

150.0

200.0

250.0

300.0

Carbon sequestered, t/ha

1

Year

Wood

products

Soil etc

Vegetation

Figure 10-3

Carbon sequestered in vegetation, soil etc. and timber products (Spanish Cedar)

Wood products can displace other products, including steel and concrete. Significant amounts of 

greenhouse gases are emitted during their production, so substitution by wood products can 

result in substantial greenhouse gas benefits. The quantity of carbon sequestered in wood 

products can reach saturation, as the rate of decomposition equals the rate of creation of new 

products but the benefits of reduced emissions from production of alternative materials 

continues. A material substitution effect of 0.28 tC/m3 of final wood product has been 

suggested5. Although this estimate is highly uncertain, it is possible that the substitution impact 

for wood products is greater than the sequestration impact. Material substitution effects are 

beyond the scope of this study.

Harvested wood can also be used as fuel, reducing greenhouse gas emissions from use of fossil 

fuels. The scope of this study was limited to carbon sequestration. Biomass fuel production is a 

broad subject in its own right and is beyond the scope of this study.

10.3.3 Non-CO2 Greenhouse Gases

Greenhouse gases other than CO2 are emitted and absorbed by forests. Forests are one of the 

main sources of nitrous oxide and they can be a source or sink for methane. Greenhouse gases 

may also be released during land clearance prior to planting of forests and during deforestation.

Little information is available on non-CO2 GHG emissions from forests. Non-CO2 greenhouse 

gases are not normally included in assessments of forestry sequestration schemes. Including 
CO2 Sink Enhancement in Forests—Basis and Approach

10-10

non-CO2 GHGs would greatly increase data requirements and monitoring costs. In this study, 

non-CO2 greenhouse gases are included as a sensitivity to indicate whether they are likely to be 

significant compared to carbon sequestration.

10.3.3.1 Nitrous Oxide from Forests and Baseline Land Uses

Global N2O sources related to land-use are summarized in Table 10-2

6

. The carbon equivalents 

of these emissions, based on a 100 year global warming potential, are also shown in Table 10-2. 

The estimated emissions of N2O from natural forests are 1.1 Gt carbon-equivalent per year, 

equivalent to about 18 percent of current emissions of CO2 from fossil fuel use. Emissions of 

N2O are enhanced under warm and wet conditions, e.g., those present in the soils of moist 

tropical forests, and when nitrogen is applied as fertilizer. The uncertainty in N2O emissions 

from forests is large, particularly for temperate forests, where the range of uncertainty is a factor 

of 16.

Table 10-2

Global N2O sources

Mt N y-1 Gt C eq. y-1

Central 

Estimate

Range Central 

Estimate

Range

Forest soils - wet tropical forests 3 2.2-3.7 0.8 0.6-1

 - temperate forests 1 0.1-2 0.3 0.03-0.5

Natural savanna soils 1 0.5-2 0.3 0.1-0.5

Natural temperate grassland soils 1 0.5-2 0.3 0.1-0.5

Cultivated soils 3.5 1.8-5.3 0.9 0.5-1.4

Biomass burning 0.5 0.2-1 0.1 0.05-0.3

Livestock 0.4 0.2-0.5 0.1 0.05-0.13

Total 10.4 2.8

10.3.3.2 Methane from Forests and Baseline Land Uses

Although the overall global emission of methane is relatively well known, the magnitude of 

individual sources and the likely changes in methane sources and sinks associated with changes 

in land use and other modifications or terrestrial ecosystems are uncertain6. Tropical wetlands 

and termites emit methane but microbial activity in soils is a significant sink for atmospheric 

methane. Overall net emissions of methane from forests compared to alternative land uses are 

estimated to be an order of magnitude less significant than emissions of N2O.
CO2 Sink Enhancement in Forests—Basis and Approach

10-11

10.3.3.3 Net Emissions of Nitrous Oxide and Methane From Forestry

Estimating the impacts on N2O and methane emissions of forestry sequestration depends on the 

emissions from the baseline land use. Forests are large emitters of N2O and in some 

circumstances are also emitters of methane, but alternative uses of the land would also emit 

significant amounts of these gases. In this study it is assumed that planted forests will replace 

pasture or abandoned farmland. If forests were planted on croplands there could be a significant 

reduction in emissions because croplands often have higher emissions than forests. However, if 

an area of cropland was forested, another area of land elsewhere may be converted to cropland to 

maintain food production, so the benefits would be illusory.

The net emissions of forests and baselines used in this study are shown in Table 10-3

7, 8

Table 10-3

Forest and baseline N2O And CH4 emissions

U.S. afforestation / 

deforestation

Non-U.S. afforestation / 

deforestation 

Avoidance of 

deforestation

CH4 emissions, kg ha-1 y-1

 Baseline -1.1 -1.1 -3.5

 Forests -1.2 -3.5 -1.1

 Net -0.1 -2.4 2.4

N2O emissions, kg ha-1 y-1

 Baseline 0.4 0.4 2.2

 Forests 1.2 2.2 0.4

 Net 0.8 1.8 -1.8

Overall emissions, tCeq ha-1 y-10.06 0.13 -0.13

Note: emissions for avoidance of deforestation are per hectare of land deforested

10.3.3.4 Emissions From Biomass Burning

Deforestation often involves open burning of biomass and some biomass is also often burned 

when ground is cleared prior to planting of forests. When vegetation is burned about 10 percent 

of the carbon contained in the biomass is converted to charcoal, which will remain stored in the 

soil for a very long time9. Most of the rest is oxidized and released to the atmosphere as CO2. 

Some methane and N2O are also produced during open burning of biomass and their total 

greenhouse gas effect is estimated to be about 0.1 tC equivalent/ tC burned, based on 100 year 

global warming potentials and emissions data9. The carbon equivalent of methane and nitrous 

oxide missions from open burning of biomass appears to be approximately equal to the quantity 

of carbon that is converted to charcoal and therefore not emitted as CO2. For this study it is 

therefore assumed that, in effect, all of the carbon released during deforestation and ground 

clearance prior to planting of forests is released as CO2.
CO2 Sink Enhancement in Forests—Basis and Approach

10-12

10.3.3.5 Other Atmospheric Emissions

Vegetation emits large quantities of volatile organic compounds (VOC) to the atmosphere, 

substantially more than are emitted from use of fossil fuels (5). VOC is also emitted during 

biomass burning, along with CO and NOx (NO and NO2). These gases have only a small direct 

impact on radiative forcing but they have a large indirect effect, as they affect formation of 

ozone, which is an important greenhouse gas, and OH, which affects the atmospheric burden of 

methane. Particulates are emitted during biomass burning and they too can have a significant 

impact on the climate. The climate impacts of these emissions are highly uncertain and Global 

Warming Potentials of these gases have not been agreed. They have therefore not been included 

in this study.

10.3.4 Costs

Cost information for each of the forestry cases has been provided by ECCM, based on published 

information and direct experience within developing countries. Details are given in Chapters 

11-17. The costs provided by ECCM are within the ranges of costs quoted in published 

references. Costs from a variety of published references are given in Appendix C for 

comparison.

10.3.4.1 Establishment and Maintenance

Establishment costs provided by ECCM range from 100 to 700 $/ha. The costs provided by 

ECCM are within the ranges of costs quoted in published references. Costs from a variety of 

published references are given in Appendix D for comparison. Costs tend to be higher in 

developed countries because of higher labor costs.

10.3.4.2 Land Costs

Land costs are based on typical marginal costs of alternative land uses. The costs range from 

zero, where the land would not otherwise be used, to 1750 $/ha.

Large-scale application of carbon sequestration would affect the balance of supply and demand, 

and hence the price of fertile land. The cost of forestry carbon sequestration would therefore 

increase as the extent of sequestration forests increases. In some places, particularly developing 

countries, land owners may be reluctant to convert their land to sequestration forests because of 

uncertainties about making long term commitments, social and cultural attachments to current 

agricultural practices, a wish to ensure production of subsistence agricultural products and 

concerns about land ownership rights. The price paid for land for forestry sequestration may 

need to be greater than the current marginal value, to encourage land owners to change to 

sequestration forestry. No attempt is being made in this study to produce cost-supply curves for 

any of the sequestration options. These issues are therefore beyond the scope of this study.
CO2 Sink Enhancement in Forests—Basis and Approach

10-13

10.3.4.3 Timber Revenues

Forests produce useful products, particularly timber and fuel wood and also in some cases food, 

medicinal and other types of plants. Revenue from the sale of timber is included as in the 

economic assessment. Other products are mainly used locally and their value is assumed to help 

ensure the long term maintenance of the forests.

In the model, timber prices in the future are assumed to be the same as at present. A similar 

assumption is made in the EOR and ECBM cases, where oil and gas prices are assumed to 

remain constant. However, it is recognized that future timber prices are uncertain and large scale 

adoption of sequestration forestry could itself significantly affect timber prices. This report 

therefore presents sequestration costs with and without timber revenues.

10.3.4.4 Monitoring

The change in carbon stocks of stands of forest trees over a 5-year period can be assessed with 

good precision through standard inventory methods. Carbon stocks in soils have been 

determined by standard sampling techniques but large numbers of samples are required to 

achieve adequate precision6. Third party organizations offer independent carbon verification 

services, to give increased confidence to purchasers of carbon credits. A combination of remote 

satellite monitoring and on-the-ground measurements is normally used.

The costs of measuring and monitoring carbon sequestration in forests are a function mainly of 

the desired level of precision, which may vary by the type of project activities, the size of the 

project and the natural variation within the various carbon pools. The level of precision has a 

very large effect on the costs, for example variable costs for a precision of ±5 percent may be a 

factor of a hundred greater than for a precision of ±30 percent6. If projects could only claim 

carbon credits for the lower bound of the confidence interval and the value of carbon credits was 

high, it would be worthwhile monitoring to a high level of precision.

A few data are available to provide preliminary estimates of the costs of measuring and 

monitoring of carbon in forestry projects. Based on available information6, an annual monitoring 

cost of $5/ha has been assumed for this study.

10.3.4.5 Financial Parameters

The same financial parameters were used for forestry options as for CO2 capture and storage 

options included in this project. The rationale is that we assume that funding for both types of 

projects would come from an energy company seeking to receive credits for reducing GHG 

emissions. An energy company likely would use the same financial parameters for evaluating 

GHG abatement investments regardless of whether the investments involve CO2 capture and 

storage or CO2 sink enhancement. Costs were estimated on a constant-dollar basis using an 

interest rate of 5.83 percent, income tax rate of 38 percent, and after-tax discount rate of 6.09 

percent (see the Section 2.4 for more details and rationale). Land, initial transaction, and 

establishment costs were levelized over the first 20 years using a 5.83 percent interest rate. 
CO2 Sink Enhancement in Forests—Basis and Approach

10-14

Taxable income was based on revenues from a given harvest minus expenses that accumulated to 

that point. If accumulated expenses were greater than accumulated revenues at a given harvest, 

then expenses that were not yet offset by revenues were carried over to the next harvest for 

calculating taxes.

Costs of GHG mitigation ($/tonne life-cycle avoided C equivalents) were calculated on an NPV 

basis as described in detail in Section 2.4. An after-tax discount rate of 6.09 percent was used to 

calculate NPV’s. Costs calculated on this basis can be compared with costs from other CO2

storage or sink enhancement options that differ in timing of costs and in timing and permanence 

of GHG mitigation.

10.4 References

1 IEA Greenhouse Gas R&D Programme, Carbon sequestration by forestry – effects of timber 

markets, IEA GHG report PH3/10, June 1999.

2 UK Met. Office Hadley Centre for Climate Prediction and Research, Climate change and its 

impacts, November 1998.

3 New Scientist, 21 July 2001, p.4-5.

4 UK Met Office, 2001, www.met-office.gov.uk

5 IPCC (Intergovernmental Panel on Climate Change), Climate change 2001, the scientific 

basis, published by Cambridge University Press, 2001, ISBN 0 521 80767 0.

6 IPCC (Intergovernmental Panel on Climate Change), Land use, land-use change, and 

forestry, published by Cambridge University Press, 2000, ISBN 0 521 80083 8.

7 Ambus P. and Christensen S., Atmospheric pollutants and trace gases – spatial and seasonal 

nitrous oxide and methane fluxes in Danish forest, grassland, and agroecosystems, 

J.Environ. Qual. 24:993-1001 (1995).

8 Weitz A.M. et.al., Nitrous oxide, nitric oxide and methane fluxes from soils following 

clearing and burning of tropical secondary forest, J.Geophysical Research, 103 (D21), 

p.28,047-28,058, Nov 20, 1998.

9 UNEP/OECD/IEA/IPCC 1997, Revised IPCC Guidelines for National GHG Inventories 

Vol. 3: GHG Inventory Reference Manual, IPCC, Bracknell, UK.
11-1

11

CASE 1: SOUTHERN U.S. LOBLOLLY PINE 

PLANTATIONS

11.1 Introduction

Loblolly pine (Pinus taeda) is a versatile, fast-growing softwood species that thrives in much of 

central and eastern US. It is the most important timber species in the United States, with an 

estimated growing stock of 1.4 billion m3 across approximately 13.4 million ha, with around 

2.4 million ha of loblolly pine plantations. In some States there has been a threefold increase in 

the area for Loblolly pine plantations since the 1970s.

11.2 Ecology

Loblolly pine grows in a variety of conditions; its natural range extends across several States, 

from 28oN in Florida, Texas and Oklahoma to 39oN in Delaware, New Jersey and Maryland. 

East-west its range stretches from 75-97°W. It is found naturally between the flat Atlantic 

coastal plains up to altitudes of 1520 m in the Appalachian Highlands. Mean annual rainfall is 

between 1020-1520 mm per year and the mean annual temperature is 13-24oC. It will grow on a 

variety of soils but grows best on moderately acid soils with imperfect to poor drainage. It does 

not normally thrive on base-rich soils with high pH.

11.3 Management Operations

Typical management operations are described in the following subsections.

11.3.1 Establishment

Establishment of Loblolly pine plantations usually involves high intensity site preparation using 

mechanical methods to remove above-ground biomass, scarification and bedding of soils and 

application of fertilizer. Herbicides may be used to control vegetation on low productivity sites 

but are less effective on high productivity sites. Planting normally takes place between 

December and March, depending on adequate soil moisture availability. Planting density is 

between 625 and 1,100 stems per ha (4x4 to 3x3m). For sites under 50 ha the unit costs of 

mechanical operations and fencing may be uneconomic.
Case 1: Southern U.S. Loblolly Pine Plantations

11-2

11.3.2 Maintenance

Maintenance of Loblolly pine plantations consists of:

• control of competing vegetation, in particular during the early years

• control of hardwoods

• application of P and N fertilizers that can significantly improve growth (no fertilizer assumed 

in this study)

11.3.3 Harvesting

Thinning can take place in years 15, 25, and 35 to remove saleable trees which would not survive 

to the end of the rotation and to release higher quality trees. Final clearcut takes place at 

45 years.

11.3.4 Re-establishment

Planting of nursery grown seedlings is the most common method of re-establishment. Artificial 

regeneration by direct seeding may also be considered as a cheaper option but is less likely to 

produce good regeneration.

11.3.5 Classification of Productivity

Productivity on the southern coastal plains is generally relatively high and declines in the shale 

and sandstone regions of the highlands. Site indices are used to indicate site quality and 

potential growth, classified by height at a reference age (usually 25 for plantations, 50 for natural 

stands). Table 11-1 shows the two productivity levels for Loblolly pine.

Table 11-1

Productivity classes for Loblolly Pine in the southern United States

Productivity Site Index Conditions Production

High 21-32 Loam hills and plains 220m3

45 years

Medium 20-23 Sand hills and plains (deep sands) 190m3

45 years

Low 18-24 Mixed sandstone shale n.a.
Case 1: Southern U.S. Loblolly Pine Plantations

11-3

11.4 Carbon Sequestration Potential

11.4.1 Baseline

The following are the initial assumptions for existing stored carbon at the site based on ECCM 

information.

• Vegetation: 5 tC/ha.

• Soils: approximately 24 tC/ha under cropland

11.4.2 Carbon Uptake and Storage by Pinus Taeda

Pinus taeda is a fast growing species with maximum annual growth from 15 to 30 years. 

Rotations vary but a 45-year cycle is common, with earlier thinnings to produce some income. 

Values selected for this case are shown in Table 11-2.

Table 11-2

Expected carbon storage from Loblolly Pine plantations over 100 years

Productivity Low Medium High

Rotation length (years) n.a. 45 45

Maximum above ground carbon (tC ha-1) n.a. 92 95

Average above-ground carbon (tC ha-1) n.a. 40 53

Average soil etc. carbon (tC ha-1) n.a. 67 72.

11.5 Costs

11.5.1 Establishment and Maintenance

Site preparation costs for intensive management can range from $370-$480/ha with chemical 

treatments ranging from $136-$395/ha. The overall average cost has been assumed to be 

$690/ha for this study. Establishment grants are available in many US states but they have not 

been included in this study. The amount of grant aid available varies between states.

The main post-establishment costs are associated with weed control (particularly in high-rainfall 

areas). The main weeds are competing broadleaved, hardwood trees; these will be treated with 

herbicides annually for the first 3 to 5 years, depending on the site concerned. The cost of each 

treatment will be $40 to $60 per hectare. A cost of $40 per year for the first 4 years has been 

assumed for this study.
Case 1: Southern U.S. Loblolly Pine Plantations

11-4

11.5.2 Land Costs

Most new plantations are likely to be established on marginal agricultural or range land, where 

the profitability of existing production (usually cattle grazing) is relatively low. Since the market 

for land in the southern US is fairly liquid, the price of land would appear to be a reasonable 

indicator of the net present value of income foregone. Land prices for medium quality range 

land and moderate to poor quality grazing land in the states of Missouri, Tennessee, Eastern 

Texas and Louisiana are in the range of $1000 to $2,500 per hectare. At the lower end of this 

range one would probably obtain below average yields of timber and carbon storage, with the 

exception of waterlogged river valleys, which are poor for cattle but could be highly productive 

as plantations.

11.5.3 Potential Income

Plantations are likely to turn out around 200m3 timber at the end of the rotation. At $22/m3, this 

gives an income of around $4,400, depending on site productivity.

11.6 Summary of Model Inputs and Results 

A summary of input variables for the base case (medium productivity) Loblolly pine is presented 

in Table 11-3. The cost of CO2 sink enhancement ($/tonne C) is presented on several accounting 

bases in Table 11-4. Cumulative changes in carbon sequestered (tonne C/ha) and costs ($/ha) are 

presented below Table 11-4 for the medium productivity case. For the high productivity case, 

the timber yield at the end of the rotation is 220 m3/ha and other input variables are the same as 

for the medium productivity case. Costs of CO2 sink enhancement ($/tonne C) are presented for 

the high productivity case on several accounting bases in Table 11-5. Cumulative changes in 

carbon sequestered (tonne C/ha) and costs ($/ha) are presented below Table 11-5 for the high 

productivity case. Data were unavailable for a low productivity case for Loblolly pine.

Costs in Tables 11-4 and 11-5 were calculated on an NPV basis as described in Chapter 2, and 

can be compared with costs for CO2 capture and storage and other sink enhancement options 

calculated on the same NPV basis. The same establishment, land, maintenance, and harvesting 

costs were assumed for medium and high productivity cases in Tables 11-4 and 11-5. This 

assumption likely resulted in a slight underestimation of costs for the high productivity case but 

the general comparison is still considered valid.
Case 1: Southern U.S. Loblolly Pine Plantations

11-5

Table 11-3

Forestry input variables for Loblolly Pine

Plantation, USA, Loblolly Pine

Plantation, USA, Douglas Fir

Plantation (Taungya), Mexico, Spanish cedar

Restoration, Mexico, Pine-oak

Restoration, Southern Africa, Miombo

Agro- forestry, Southern India, Mango- tamarind

Avoidance of deforestation, Mexico,Various

Productivity Medium

Interest Rate 5.83%

Tax Rate 38%

Discount rate 6.09%

Transaction Costs ($/ha) 5

Monitoring/ verification ($/ha/y) 5

Management: Plantation

Country: USA

Trees: Loblolly Pine

Establishment cost ($/ha) 690

Land cost ($/ha) 1,750

Annual maintenance ($/ha) 50

Maintenance (years) 4

Timber revenue ($/m3) 22

Time Between

Timber yield (m3/ha) : Harvests (yrs)

End of rotation 190 45

Thinning 1

Thinning 2

Forestry: Input Variables
Case 1: Southern U.S. Loblolly Pine Plantations

11-6

Table 11-4

Forestry summary of results for Loblolly Pine medium productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Plantation

Country/Region: USA

Trees: Loblolly Pine

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 129.85

Above & below ground carbon 64.62

Above & below ground & timber product carbon 59.24

Above & below ground carbon & non-CO2 GHGs 66.28

Above ground carbon with timber revenues 115.94

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All )* 54.13

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

Medium Productivity

-50

0

50

100

150

200

0 20 40 60 80 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

0 20 40 60 80 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 1: Southern U.S. Loblolly Pine Plantations

11-7

Table 11-5

Forestry summary of results for Loblolly Pine high productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Plantation

Country/Region: USA

Trees: Loblolly Pine

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 84.25

Above & below ground carbon 48.37

Above & below ground & timber product carbon 44.86

Above & below ground carbon & non-CO2 GHGs 49.29

Above ground carbon with timber revenues 74.27

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All )* 40.24

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

High Productivity

-50

0

50

100

150

200

250

0 20 40 60 80 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

-2000

-1000

0

1000

2000

3000

4000

5000

6000

7000

8000

0 20 40 60 80 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 1: Southern U.S. Loblolly Pine Plantations

11-8

11.7 References

These references are not indicated specifically in the body of the chapter and instead represent 

background for the information presented in this chapter.

1 Birdsey, Richard, A. 1996. Carbon Storage for Major Forest Types and Regions in the 

Conterminous United States, in Forests and Global Change: Vol. 2. Forest Management 

Opportunities for Mitigating Carbon Emissions. American Forests, Washington, DC.

2 Burns, Russell M., and Barbara H. Honkala, (tech. ords.) 1990. Silvics of North America: 

Vol.1. Conifers. Agriculture Handbook 654. U.S. Department of Agriculture, Forest Service, 

Washington, DC.

3 LDAF. 2000. Quarterly Report of Forest Products.

4 Howard, James L. 1999. U.S. Timber Production, Trade, Consumption and Price Statistics 

1965-1997. General Technical Report GPL-GTR-116. U.S. Department of Agriculture, 

Forest Service, Washington, DC.

5 Schultz, Robert P. 1997. Loblolly pine: the ecology and culture of loblolly pine (Pinus taeda 

L.). Agriculture Handbook 713. U.S. Department of Agriculture, Forest Service, 

Washington DC.

6 Timber-Mart South – Market Newsletter. University of Georgia, Athens, GA. 

http://www.forestry.uga.edu/warnell/tmart/
12-1

12

CASE 2: PACIFIC NORTHWEST DOUGLAS FIR 

PLANTATIONS

12.1 Introduction

Douglass fir (Pseudotsuga menziesii) occurs naturally across a large area of western North 

America. Coastal Douglas fir is a valuable timber tree, with very high yields over long rotations 

and has been planted extensively. Old-growth stands are of considerable amenity and 

biodiversity value. Douglas fir plantations have the potential to sequester large amounts of 

carbon, and new planting may help protect old-growth forests under threat of exploitation.

12.2 Ecology

Pseudotsuga menziesii is a highly valuable timber tree, which has been extensively planted 

outside its natural range in western North America. There are two varieties, Rocky Mountain 

Douglas fir (P. menziesii var. glauca) and Coastal Douglas fir (P. menziesii var. menziesii). The 

latter extends from central British Columbia, southwards to a latitude of 34° 44’N. It is the most 

dominant tree species in the Pacific West and occurs from near sea level up to 1,500m, often in 

pure stands. It grows best on well-aerated, deep soils with a pH between 5-6. It does not do well 

on poorly drained, oligotrophic soils. The Pacific Northwest has a maritime climate 

characterised by mild wet winters and cool dry summers, with annual average temperatures of 9-

13°C and annual rainfall between 380-3,000 mm. Douglas fir will not tolerate frost below -10°C 

for periods longer than a week. It is initially relatively slow growing and attains the largest 

height increments between 20-30 years. It can however maintain relatively rapid rates of growth 

for over a century. Long rotation times of up to 100 years can produce high yields and high 

quality timber. Douglas fir timber has a variety of uses including timbers, pilings, railroad ties, 

posts and poles, flooring, veneers, pulp and furniture.

12.3 Management Operations

Typical management operation practices are described in the following subsections.

12.3.1 Establishment

Douglas fir plantations are most readily established using nursery-grown seedlings. Light shade 

improves survival of first-year seedlings but thereafter control of competing vegetation is 
Case 2: Pacific Northwest Douglas Fir Plantations

12-2

essential. Planting is at densities between 750 and 1,500 stems per hectare (3.6 x 3.6 to 2.6 x 

2.6m spacing).

12.3.2 Maintenance

Maintenance of Douglas fir plantations includes:

• weed control

• fertiliser application

12.3.3 Harvesting

Rotations are 100 years +, i.e., beyond the timescale of this study. Pre-harvest thinnings at 50 

and 70 years will allow improved growth of remaining trees and provide interim income.

12.3.4 Re-establishment

Regeneration is usually by planting of nursery-stock, due to the requirements by forest laws for 

prompt regeneration of harvested areas.

12.4 Classification of Productivity

Productivity is classified by site index, according to height at a base year (usually 50 or 100 for 

Douglas fir)

Table 12-1

Productivity classes for Douglas Fir in pacific northwest USA

Productivity Site Index Conditions Production

High 25+ Annual precipitation: >1500 mm

Soils: well-aerated, deep soils

>1000m3

100 years

Medium 18-24 Intermediate >700m3

100 years

Low n/a Annual precipitation: <1000 mm

Soils: poorly drained, nutrient poor soils

Not available
Case 2: Pacific Northwest Douglas Fir Plantations

12-3

12.5 Carbon Sequestration Potential

12.5.1 Baseline

It is assumed that plantations will be established on grassland or open ground. However, for 

actual projects, caution should be taken to assess whether sites are part of an established 

plantation cycle. The following are the initial assumptions for existing stored carbon at the site 

based on ECCM information.

• Vegetation: 5 tC/ha.

• Soils: approximately 37 tC/ha under cropland

12.5.2 Carbon Uptake and Storage by Pseduotsuga Menziesii

Values selected for this case are shown in Table 12-2.

Table 12-2

Expected carbon storage from Douglas Fir plantations over 100 years

Productivity Low Medium High

Rotation length (years) n.a. 100 100

Maximum above ground carbon (tC ha-1) n.a. 258 352

Average above-ground carbon (tC ha-1) n.a. 132 181

Average soil etc. carbon (tC ha-1) n.a. 137 147

12.6 Costs

12.6.1 Establishment and Maintenance

Establishment costs for Douglas fir plantations are around $400 per hectare. However, where 

there is a strong presence of Himalayan blackberry, Scotch broom or other weeds, the forest 

manager may well incur weeding costs of $200 to $300 per hectare over and above the planting 

costs. An establishment cost of $500 per hectare has been assumed for this study.

The maintenance costs for Douglas fir are considerably lower than for Southern pines. A cost of 

$20/ha per year over 4 years has been assumed for this study.

12.6.2 Land

A net present opportunity cost of around $800 per hectare was selected for this case based on 

judgment and available existing information.
Case 2: Pacific Northwest Douglas Fir Plantations

12-4

12.6.3 Potential Income

High quality Douglas fir timber is a valuable commodity. Thinnings at 50 and 70 years can 

produce a total of about 350 m3/ha, giving an income of $33,000 - $80,000 at current prices, with 

a final felling generating $200,000 - $400,000.

12.7 Summary of Model Inputs and Results

A summary of input variables for the base case (medium productivity) Douglas fir is presented in 

Table 12-3. The cost of CO2 sink enhancement ($/tonne C) is presented on several accounting 

bases in Table 12-4. Cumulative changes in carbon sequestered (tonne C/ha) and costs ($/ha) are 

presented below Table 12-4 for the medium productivity case. For the high productivity case, 

the timber yields are 175 and 225 m3/ha for thinnings 1 and 2, respectively, and other input 

variables are the same as for the medium productivity case. Costs of CO2 sink enhancement 

($/tonne C) are presented for the high productivity case on several accounting bases in Table 12-

5. Cumulative changes in carbon sequestered (tonne C/ha) and costs ($/ha) are presented below 

Table 12-5 for the high productivity case. Data were unavailable for a low productivity case for 

Douglas fir.

Costs in Tables 12-4 and 12-5 were calculated on an NPV basis as described in Chapter 2, and 

can be compared with costs for CO2 capture and storage and other sink enhancement options 

calculated on the same NPV basis. The same establishment, land, maintenance, and harvesting 

costs were assumed for medium and high productivity cases in Tables 12-4 and 12-5. This 

assumption likely resulted in a slight underestimation of costs for the high productivity case but 

the general comparison is still valid. 
Case 2: Pacific Northwest Douglas Fir Plantations

12-5

Table 12-3

Forestry input variables for Douglas Fir

Plantation, USA, Loblolly Pine

Plantation, USA, Douglas Fir

Plantation (Taungya), Mexico, Spanish cedar

Restoration, Mexico, Pine-oak

Restoration, Southern Africa, Miombo

Agro- forestry, Southern India, Mango- tamarind

Avoidance of deforestation, Mexico,Various

Productivity Medium

Interest Rate 5.83%

Tax Rate 38%

Discount rate 6.09%

Transaction Costs ($/ha) 5

Monitoring/ verification ($/ha/y) 5

Management: Plantation

Country: USA

Trees: Douglas Fir

Establishment cost ($/ha) 500

Land cost ($/ha) 800

Annual maintenance ($/ha) 20

Maintenance (years) 4

Timber revenue ($/m3) 150

Time Between

Timber yield (m3/ha) : Harvests (yrs)

End of rotation

Thinning 1 135 50

Thinning 2 215 70

Forestry: Input Variables
Case 2: Pacific Northwest Douglas Fir Plantations

12-6

Table 12-4

Forestry summary of results for Douglas Fir medium productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Plantation

Country/Region: USA

Trees: Douglas Fir

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 47.58

Above & below ground carbon 23.21

Above & below ground & timber product carbon 22.79

Above & below ground carbon & non-CO2 GHGs 23.61

Above ground carbon with timber revenues 14.01

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All )* 6.83

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

Medium Productivity

-50

0

50

100

150

200

250

300

350

400

450

500

0 20 40 60 80 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

-35000

-30000

-25000

-20000

-15000

-10000

-5000

0

5000

0 20 40 60 80 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 2: Pacific Northwest Douglas Fir Plantations

12-7

Table 12-5

Forestry summary of results for Douglas Fir high productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Plantation

Country/Region: USA

Trees: Douglas Fir

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 33.55

Above & below ground carbon 18.66

Above & below ground & timber product carbon 18.35

Above & below ground carbon & non-CO2 GHGs 18.92

Above ground carbon with timber revenues 4.99

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All )* 2.76

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

High Productivity

-100

0

100

200

300

400

500

600

0 20 40 60 80 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

-40000

-35000

-30000

-25000

-20000

-15000

-10000

-5000

0

5000

0 20 40 60 80 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 2: Pacific Northwest Douglas Fir Plantations

12-8

12.8 References

These references are not indicated specifically in the body of the chapter and instead represent 

background for the information presented in this chapter.

1 Birdsey, Richard, A. 1996. Carbon Storage for Major Forest Types and Regions in the 

Conterminous United States, in Forests and Global Change: Vol. 2. Forest Management 

Opportunities for Mitigating Carbon Emissions. American Forests, Washington, DC.

2 Burns, Russell M., and Barbara H. Honkala, (tech. ords.) 1990. Silvics of North America: 

Vol.1. Conifers. Agriculture Handbook 654. U.S. Department of Agriculture, Forest 

Service, Washington, DC.

3 Howard, James L. 1999. US Timber Production, Trade, Consumption and Price Statistics 

1965-1997. General Technical Report GPL-GTR-116. US Department of Agriculture, 

Forest Service, Washington, DC.
13-1

13

CASE 3: SPANISH CEDAR PLANTATIONS IN MEXICO 

USING THE TAUNGYA SYSTEM

13.1 Introduction

The “Taungya system” for establishing plantations of Spanish cedar or “cedro” (Cedrela 

odorata) reduces the opportunity costs of plantation establishment by allowing the farmer to 

grow annual crops between the trees during the initial years. The annual crops provide some 

additional income and the saplings benefit from the maintenance of the crops. Furthermore, as 

the planting density is lower than in a mono-culture plantation, the cost of buying seedlings is 

reduced. After 3-4 years the tree will shade-out the annual crops. This system is best suited to 

areas of higher rainfall where competition for water is not a problem, and where there is 

sufficient labor to carry out the cropping and tree maintenance activities by hand.

13.2 Ecology

Cedrela odorata is a fast-growing, high-value timber tree that is native to large areas of Central 

and South America from southern Mexico to Bolivia, 28oS–26oN. It is found naturally at 

altitudes between 0 and 1500 m, where mean annual rainfall is between 1200-3000 mm per year, 

and where the mean annual temperature is 20-32oC. It will grow on light, medium or heavy soils 

but thrives on free draining, fertile soils. Cedrela odorata is a light demanding species that 

develops best in open spaces or large clearings in the highly diverse tropical broadleaved forests 

but in much of its native range the gene pool has been severely depleted due to the high demand 

for its valuable timber. Cedrela odorata is grown as a plantation tree throughout the tropics.

13.3 Management Operations

The primary objective of the Taungya system is timber production. Crop yields (maize for this 

study) during the first few years will help cover planting costs. Thinning is used to liberate 

selected individuals of good form. Cedrela odorata produces a very high quality, valuable 

timber for which there is a ready market. Although plantations have lower biodiversity value, 

the production of this species may lessen pressure on surviving populations in native forests. 

Soil conservation is improved on steep slopes. Typical management operations are described in 

the following subsections.
Case 3: Spanish Cedar Plantations in Mexico Using the Taungya System

13-2

13.3.1 Establishment

Techniques used for planting maize are used to prepare the site for establishing the tree crop / 

maize mixture. Although these techniques vary with location the following activities are 

undertaken:

• Clearing weeds and other vegetation

• Sowing maize (according to local practices)

• Making holes for seedlings – large holes 30cm diameter and depth produce better conditions 

for root development, the topsoil is more fertile and should be placed in the bottom of the 

hole for better rooting.

• In very compact soils holes may be dug after the start of the rains.

It is important to obtain good quality planting stock, which should be ready for planting at the 

beginning of the rainy season. Planting a mixture of species as well as Cedrela odorata may 

help reduce the occurrence of the shoot borer Hypsipyla grandela. Note if other species used 

have similar growth rates or account for not more than 20 percent of the crop, carbon estimates 

will not be significantly affected. Planting density should be between 1100 and 625 stems per ha 

(3x3 to 4x4m). The roots of seedlings should be pruned just prior to planting to help root 

development

13.3.2 Maintenance

• Weeding should be carried out at least twice per year until canopy closure.

• Pruning is vital to maintain tree form, whenever is there is evidence of attack by Hypsipyla 

grandela

The most important pest of Cedrela odorata and one of the most important factors in 

establishment is the shoot borer Hypsipyla grandela. The larvae of the moth develop in the 

apical bud of young trees causing the shoot to die. This does not kill the tree but leads to the 

growth of several subsidiary shoots. If not pruned this will lead to forking of the main stem and 

drastically reduce the value of the timber. However if all but the strongest, straightest shoots that 

result are removed the tree will continue to grow with good form and the length of saleable 

timber will be much increased. Pruning of subsidiary shoots must take place within one or two 

years of Hypsipyla attack as the removal of older branches will put the tree at risk of disease. 

One means of reducing the occurrence of the shoot borer is to use a mixture of species.

Although not conclusively proven, inter-planting of other fast growing species with the Cedrela 

may help prevent infestation through reducing the chances of the moth finding its host in the 

plantation. Chemical and biological means of control do exist but the cost might make these 

unfeasible. The great advantage in small- scale plantations is that the farmer can quickly spot 

damaged trees and prune the shoots where necessary during routine maintenance. After 

approximately 5 years the trees become less susceptible to the shoot borer. It is extremely 

important that farmers are given training in pruning trees if they are to realize the full value of 

this species.
Case 3: Spanish Cedar Plantations in Mexico Using the Taungya System

13-3

13.3.3 Harvesting

Harvesting should take place in year 25, however in poorer sites the rotation length may be up to 

30 years.

13.3.4 Re-establishment

Two methods of re-establishment may be used in subsequent rotations:

1. Shelterwood: 25 to 30 trees/ha (approx. 20x20m) may be retained as seed trees when the 

main crop is felled to provide seed for the new crop. Regeneration should be maintained by 

regular weeding.

2. Repetition of the taungya system: if the crop is clear felled then the taungya system of 

establishment with annual crops may be repeated.

13.4 Classification of Productivity

Productivity is classified on three levels (low, medium and high): according to the altitude, 

rainfall and soil conditions shown in Table 13-1. Production figures are in cubic metres of 

timber harvested at the end of the specified rotation.

Table 13-1

Productivity classes for Spanish Cedar in central America

Productivity Conditions Production

High

Altitude: 300-1200 m 

Rainfall: 1450 – 3000 mm/y

Soil: Well-drained, brown-black

450 m3

25y

Medium

Altitude: <300 or >1200 m 

Rainfall: <1450 mm/y

Soil: Heavy clays; sandy

400 m3

25 y

Low

Altitude: <300 or >1200 m 

Rainfall: <1450 mm/y

Soil: Stony, compacted or oxidised clays

350 m3

30 y

13.5 Carbon Sequestration Potential

13.5.1 Baseline

The baseline conditions for Taungya systems in Central America vary considerably: most are 

established on land that is marginal for agricultural crops, where the land is cultivated 

sporadically, perhaps 2 years out of 10 using slash and burn methods. For most of the interim 
Case 3: Spanish Cedar Plantations in Mexico Using the Taungya System

13-4

period the land is covered with a low, tangled thicket, containing a mixture of herbs, shrubs, and 

small trees with the occasional large or very large tree.

Because of this diversity it is necessary to assess the conditions in each plot individually. 

Table 13-2 gives a rapid method for assessing the components of baseline carbon-density prior to 

establishment of Taungya on individual plots.

Table 13-2

Components of baseline carbon stock (tC)

Vegetation None

<10% cover

Few

10-20% cover

Moderate

20-60% cover

Abundant

>60% cover

Herbs 0 0.4 1.2 2.0

Shrubs 0 0.8 2.4 4.0

Small trees 1-5m 0 1.4 4.2 12.0

Medium trees 5-10m 0 2.4 7.2 18.0

Large trees 10-20m 1 tC per tree

Very large trees >20m 3 tC per tree

For the purposes of this assessment the baseline vegetation carbon content was assumed to be 

8.6 tC/ha.

13.5.2 Carbon Uptake and Storage by Cedrela Odorata

Following the first 3 years of establishment, the system will accumulate carbon rapidly at 8 to 15 

tC ha-1 y-1 during most of the 25-30 year rotation. Carbon storage for different productivity 

classes will be approximately as shown in Table 13-3:

Table 13-3

Expected carbon storage from Spanish Cedar over 100 years

Productivity Low Medium High

Rotation length (years) 30 25 25

Maximum above ground carbon (tC ha-1) 126 148 173

Average above-ground carbon (tC ha-1) 68 82 102

Average soil etc. carbon (tC ha-1) 121 132 147
Case 3: Spanish Cedar Plantations in Mexico Using the Taungya System

13-5

13.6 Costs

13.6.1 Establishment and Maintenance

The cost of implementation by small farmers in Chiapas, southern Mexico (2000) was estimated 

at $385/ha. In the first three years most of the weeding costs are paid for by the cultivation of 

maize between the young trees. The only other significant cost is pruning of trees attacked by 

shoot borer. The cost depends upon the severity of the attack and could be anything from zero in 

unaffected plantations to $50 per hectare per year up to year 7 on severely affected plots. A cost 

of $25/ha for 7 years was assumed for this study.

13.6.2 Land Cost

These systems are generally established on marginal agricultural land, where slope or soil 

conditions are sub-optimal for maize production. The opportunity cost (lost production from 

land) is estimated at $0-1350/ha, depending upon site quality (agricultural production on poor 

quality sites has virtually zero opportunity cost). An average value of $675/ha was chosen for 

this study.

13.6.3 Potential Income

Potential income from timber production of 400 m3/ha at a price of $100 /m3 gives a total of 

$40k $/ha per rotation.

13.7 Summary of Model Inputs and Results 

A summary of input variables for the base case (medium productivity) Spanish cedar is presented 

in Table 13-4. The cost of CO2 sink enhancement ($/tonne C) is presented on several accounting 

bases in Table 13-5. Cumulative changes in carbon sequestered (tonne C/ha) and costs ($/ha) are 

presented below Table 13-5 for the medium productivity case. For the low productivity case, the 

timber yield at the end of the rotation is 350 m3/ha, time between harvest is 30 years, and other 

input variables are the same as for the medium productivity case. Costs of CO2 sink 

enhancement ($/tonne C) are presented for the low productivity case on several accounting bases 

in Table 13-6. Cumulative changes in carbon sequestered (tonne C/ha) and costs ($/ha) are 

presented below Table 13-6 for the high productivity case. For the high productivity case, the 

timber yield at the end of the rotation is 450 m3/ha, time between harvest is 25 years, and other 

input variables are the same as for the medium productivity case. Costs of CO2 sink 

enhancement ($/tonne C) are presented for the high productivity case on several accounting 

bases in Table 13-7. Cumulative changes in carbon sequestered (tonne C/ha) and costs ($/ha) are 

presented below Table 13-7 for the high productivity case.

Costs in Tables 13-5 to13-7 were calculated on an NPV basis as described in Chapter 2, and can 

be compared with costs for CO2 capture and storage and other sink enhancement options 

calculated on the same NPV basis. The same establishment, land, maintenance, and harvesting 
Case 3: Spanish Cedar Plantations in Mexico Using the Taungya System

13-6

costs were assumed for medium, low, and high productivity cases in Tables 13-5 to 13-7. This 

assumption likely resulted in a slight overestimation of costs for the low productivity case and a 

slight underestimation of costs for the high productivity case but the general comparison is still 

valid.

In the graphs associated with Tables 13-5 to 13-7, the change in aboveground carbon becomes 

negative at the beginning of the accounting period. The initial negative change in aboveground 

carbon is due to clearing of some existing vegetation in preparation for establishment of the 

Spanish Cedar plantation. The cumulative change in aboveground carbon becomes positive as 

the new tree growth more than compensates for the clearing of vegetation before planting.

Table 13-4

Forestry input variables for Spanish Cedar

Plantation, USA, Loblolly Pine

Plantation, USA, Douglas Fir

Plantation (Taungya), Mexico, Spanish cedar

Restoration, Mexico, Pine-oak

Restoration, Southern Africa, Miombo

Agro- forestry, Southern India, Mango- tamarind

Avoidance of deforestation, Mexico,Various

Productivity Medium

Interest Rate 5.83%

Tax Rate 38%

Discount rate 6.09%

Transaction Costs ($/ha) 5

Monitoring/ verification ($/ha/y) 5

Management: Plantation (Taungya)

Country: Mexico

Trees: Spanish cedar

Establishment cost ($/ha) 385

Land cost ($/ha) 675

Annual maintenance ($/ha) 25

Maintenance (years) 7

Timber revenue ($/m3) 100

Time Between

Timber yield (m3/ha) : Harvests (yrs)

End of rotation 400 25

Thinning 1

Thinning 2

Forestry: Input Variables
Case 3: Spanish Cedar Plantations in Mexico Using the Taungya System

13-7

Table 13-5

Forestry summary of results for Spanish Cedar medium productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Plantation (Taungya)

Country/Region: Mexico

Trees: Spanish cedar

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 23.68

Above & below ground carbon 14.35

Above & below ground & timber product carbon 12.81

Above & below ground carbon & non-CO2 GHGs 14.67

Above ground carbon with timber revenues (101.36)

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All)* (55.88)

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

Medium Productivity

-50

0

50

100

150

200

250

300

0 10 20 30 40 50 60 70 80 90 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

-120000

-100000

-80000

-60000

-40000

-20000

0

20000

0 10 20 30 40 50 60 70 80 90 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 3: Spanish Cedar Plantations in Mexico Using the Taungya System

13-8

Table 13-6

Forestry summary of results for Spanish Cedar low productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Plantation (Taungya)

Country/Region: Mexico

Trees: Spanish cedar

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 29.66

Above & below ground carbon 17.95

Above & below ground & timber product carbon 16.42

Above & below ground carbon & non-CO2 GHGs 18.46

Above ground carbon with timber revenues (68.69)

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All)* (39.01)

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

Low Productivity

-50

0

50

100

150

200

250

0 10 20 30 40 50 60 70 80 90 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

-70000

-60000

-50000

-40000

-30000

-20000

-10000

0

10000

0 10 20 30 40 50 60 70 80 90 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 3: Spanish Cedar Plantations in Mexico Using the Taungya System

13-9

Table 13-7

Forestry summary of results for Spanish Cedar high productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Plantation (Taungya)

Country/Region: Mexico

Trees: Spanish cedar

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 18.42

Above & below ground carbon 11.22

Above & below ground & timber product carbon 10.11

Above & below ground carbon & non-CO2 GHGs 11.41

Above ground carbon with timber revenues (90.70)

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All)* (50.55)

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

High Productivity

-50

0

50

100

150

200

250

300

350

0 10 20 30 40 50 60 70 80 90 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

-120000

-100000

-80000

-60000

-40000

-20000

0

20000

0 10 20 30 40 50 60 70 80 90 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 3: Spanish Cedar Plantations in Mexico Using the Taungya System

13-10

13.8 Reference

These references are not indicated specifically in the body of the chapter and instead represent 

background for the information presented in this chapter.

1 Mayhew J.E. and Newton A.C. (1998) The Silviculture of Mahogany. CABI Publishing, 

Wallingford, UK.

2 Webb D.B., Wood P.J., Smith J.P. and Henman G.S. (1984) A Guide to Species Selection 

for Tropical and Subtropical Plantations. Tropical Forestry Paper 15, Oxford, UK.

3 CABI Forestry Compendium.

4 ECCM technical note for classifying productivity in the absence of yield class information.

5 Data adapted from Tipper R., de Jong B., Ochoa-Gaona S., Soto-Pinto M., Castillo-Santiago 

M., Montoya-Gomez G. and March-Mifsut I. (1999) Assessment of the cost of large scale 

forestry for CO2 sequestration: evidence from Chiapas, Mexico. IEA Greenhouse Gas 

R&D Programme.

6 de Jong unpublished data.

7 Montoya G., Soto L., de Jong B., Nelson K., Farias P., Taylor J. and Tipper R. (1995) 

Desarrollo forestall sustenable: captura de carbono en las zones tzeltal y tojolabal del estado 

de Chiapas. Cuadernos de Trabajo 4, Instituto Nacional de Ecología, Mexico.

8 Cibrian 1995.
14-1

14

CASE 4: RESTORATION OF DEGRADED PINE-OAK 

WOODLAND IN CHIAPAS, MEXICO THROUGH 

ENRICHMENT PLANTING WITH PINUS OOCARPA

14.1 Introduction

This case is based on a system used in the central highlands of Chiapas, Mexico. It involves the 

management of secondary pine/oak vegetation for the production of timber, fuelwood and other 

products through enrichment planting with Pinus oocarpa and liberation thinning to encourage 

the growth of naturally regenerating oak (Quercus sp).

Pinus oocarpa produces good quality timber. There is a ready market for both round and sawn 

wood, and it is also used locally for house construction. Oak trees of good form can produce a 

high value timber and it is also the preferred species for fuelwood. Other species present are 

used for various purposes including poles, stakes, fruits and flowers. Regenerating pine/oak 

forest has a high biodiversity value due to the variety of tree species and other flora and fauna 

present. Soil conservation is improved on steep slopes.

14.2 Ecology1,2,3,4

Pine-oak forest occurs naturally in temperate to sub-tropical mountain regions of Central 

America. The composition of pine-oak forests is extremely varied and different mixtures of at 

least 5 species of oak, the most common, and economically important of which are Q.

segouiensis (roble) and Q. crispipilis (chicinb), and 4 species of pine occur in different locations 

in the highlands of southern Mexico. Well-preserved areas of forest are noted for their diverse 

flora and fauna, including bromeliads, orchids and hummingbirds. However, these highland 

areas are among the most densely populated regions of Central America and the composition and 

structure of these forests are heavily influenced by human activities, such as slash and burn 

agriculture, timber harvesting browsing by sheep and goats and the collection of firewood. In 

many cases the forests have been severely degraded, such that only scrubby vegetation remains.

Pinus oocarpa is one of the native pine species of the region. It occurs over a wide range of 

altitude, 250-2500 m, and rainfall, 700-3000 mm/yr. It grows best between 700 and 2000 m and 

with 1200-2000 mm rain/yr on free draining soils1 and will tolerate shallow or infertile soils and 

steep slopes. The mean annual temperature in its na tural range is 13-21oC with a mean range of 

8-32oC from the hottest to coldest month.
Case 4: Restoration of Degraded Pine-Oak Woodland in Chiapas, Mexico Through Enrichment Planting With 

Pinus oocarpa

14-2

14.3 Management Operations

Typical management operations are described in the following subsections.

14.3.1 Establishment

Planting lines at 2 m widths are cleared through the existing secondary vegetation to reduce 

competition for light. It is recommended to cut these lines on an east-west axis to maximize the 

available sunlight. The planting lines are cleared of all herbaceous and shrubby vegetation and 

all overhanging branches are cut back; planting lines may be diverted around larger trees to save 

labour.

Holes are dug for seedlings (30cm depth and 30cm diameter) – large holes produce better 

conditions for root development. Seedlings are planted at a density between 5-700 stems /ha 

(7x2 to 7x3m). The roots of seedlings are pruned just prior to planting to help root development. 

It is important to obtain good quality planting stock, which should be ready for planting at the 

beginning of the rainy season.

14.3.2 Maintenance

Weeding is carried out twice per year until canopy closure. Pruning is carried out when 

necessary to prevent forking and to reduce lateral branching. Planting lines are maintained clear 

of overhanging branches until the pine trees reach the height of surrounding secondary 

vegetation.

14.3.3 Harvesting

The harvest takes place in year 30-35 when the trees have a diameter of 40cm.

14.3.4 Re-establishment

Two methods of re-establishment may be used in subsequent rotations:

1. Shelterwood: 25 to 30 trees per ha (approx. 20x20 m) may be retained as seed trees when the 

main crop is felled to provide seed for the new crop. Regeneration should be maintained by 

regular weeding.

2. Re-establishment through the taungya system. An increase in soil fertility may make the plot 

suitable for replanting with pine seedlings combined with cultivation of maize for the first 

3-4 years.
Case 4: Restoration of Degraded Pine-Oak Woodland in Chiapas, Mexico Through Enrichment Planting With 

Pinus oocarpa

14-3

14.4 Classification of Productivity

In Table 14-1, productivity is classified according to climatic and soil conditions1; site 

productivity is classified by soil characteristics (under development) and the production of 

maize5.

Table 14-1

Productivity classes for Pinus Oocarpa in Chiapas, Mexico

Productivity Conditions Production

High Altitude: 700 - 2000 m

Rainfall: 1500 - 2000 mm

Soil: Well-drained sandy loams, >30cm depth

320 m3

30 y

Medium Altitude: 700 - 2000 m

Rainfall: 1200 - 1500 mm

Soil: Intermediate

280 m3

35 y

Low Altitude: <700 or >2000 m

Rainfall: <1200 mm/y

Soil: Impermeable clays or stony <20cm depth

220 m3

35 y

14.5 Carbon Sequestration Potential

14.5.1 Baseline

Since the condition of the forest resource at the start of a project will vary from place to place it 

is important to assess the baseline carbon storage at each site. Table 14-2 provides a rapid 

method for estimating baseline carbon density at year 0.
Case 4: Restoration of Degraded Pine-Oak Woodland in Chiapas, Mexico Through Enrichment Planting With 

Pinus oocarpa

14-4

Table 14-2

Components of baseline carbon stock (tC)

Vegetation

None

<10% cover

Few

10-20% cover

Moderate

20-60% cover

Abundant

>60% cover

Herbs 0 0.4 1.2 2.0

Shrubs 0 0.8 2.4 4.0

Small trees 1-5m 0 1.4 4.2 12.0

Medium trees 5-10m 0 2.4 7.2 20.0

Large trees 10-20m 0.8 tC per tree

Very large trees >20m 2.5 tC per tree

For the purposes of this study the baseline vegetation carbon content was assumed to be 

20.6 tC/ha.

Since most semi-degraded pine-oak forests in the central highlands of Chiapas are in a process of 

gradual deterioration, it is reasonable to expect further loss of biomass in the baseline scenario, 

of at least 1 percent per year. This rate of loss could be exceeded in areas where there is a high 

level of population pressure and ease of access. However, it is recommended that rates of loss of 

carbon stocks in excess of 1 percent per year should be substantiated using an objectively 

verifiable methodology such as a regional risk matrix model (RRM) as discussed for Case 7 

(avoidance of deforestation).

14.5.2 Carbon Uptake by the Regenerating Forest

Following the first 4 years of establishment, the system will accumulate carbon at 5 to 

12 tC ha-1y

-1

 during most of the 30-35 year rotation. Carbon storage for different productivity 

classes will be approximately as shown in Table 14-3:

Table 14-3

Expected carbon storage from Pine-Oak forest enriched with Pinus Oocarpa over 100 

years

Productivity Low Medium High

Rotation length (years) 35 35 30

Maximum above ground carbon (tC ha-1) 73 98 112

Average above-ground carbon (tC ha-1) 44 61 67

Average soil etc. carbon (tC ha-1) 99 114 121
Case 4: Restoration of Degraded Pine-Oak Woodland in Chiapas, Mexico Through Enrichment Planting With 

Pinus oocarpa

14-5

14.6 Costs:

14.6.1 Establishment and Maintenance

The cost of initial establishment has been estimated at $290 per hectare followed by annual 

maintenance costs of around $60 per year for a further 6 to 8 years, depending on the state of the 

vegetation into which the pine is introduced. From then on any maintenance costs are likely to 

be covered by the value of fuelwood, fenceposts, and other minor forest products.

14.6.2 Land

The net present opportunity cost of land associated with this system is generally near to zero, 

since the alternative use would be marginal grazing on the degraded forest with perhaps the 

occasional crop of maize in patches of bare ground. A conservatively low value of $200/ha was 

chosen for this study.

14.6.3 Potential Income

300 m3 of pine timber/ha could be produced, at a price of $30/m3, giving a total income of 

$9000/ha at end of rotation plus additional benefits from fuelwood and other products. The 

volume of timber is estimated from the average reported yield2.

14.7 Summary of Model Inputs and Results 

A summary of input variables for the base case (medium productivity) Pine oak is presented in 

Table 14-4. The cost of CO2 sink enhancement ($/tonne C) is presented on several accounting 

bases in Table 14-5. Cumulative changes in carbon sequestered (tonne C/ha) and costs ($/ha) are 

presented below Table 14-5 for the medium productivity case. For the low productivity case, the 

timber yield at the end of the rotation is 220 m3/ha, time between harvest is 35 years, and other 

input variables are the same as for the medium productivity case. Costs of CO2 sink 

enhancement ($/tonne C) are presented for the low productivity case on several accounting bases 

in Table 14-6. Cumulative changes in carbon sequestered (tonne C/ha) and costs ($/ha) are 

presented below Table 14-6 for the high productivity case. For the high productivity case, the 

timber yield at the end of the rotation is 320 m3/ha, time between harvest is 30 years, and other 

input variables are the same as for the medium productivity case. Costs of CO2 sink 

enhancement ($/tonne C) are presented for the high productivity case on several accounting 

bases in Table 14-7. Cumulative changes in carbon sequestered (tonne C/ha) and costs ($/ha) are 

presented below Table 14-7 for the high productivity case.

Costs in Tables 14-5 to 14-7 were calculated on an NPV basis as described in Chapter 2, and can 

be compared with costs for CO2 capture and storage and other sink enhancement options 

calculated on the same NPV basis. The same establishment, land, maintenance, and harvesting 

costs were assumed for medium, low, and high productivity cases in Tables 14-5 to 14-7. This 

assumption likely resulted in a slight overestimation of costs for the low productivity case and a 
Case 4: Restoration of Degraded Pine-Oak Woodland in Chiapas, Mexico Through Enrichment Planting With 

Pinus oocarpa

14-6

slight underestimation of costs for the high productivity case but the general comparison is still 

considered valid.

Table 14-4

Forestry input variables for Pine-Oak

Plantation, USA, Loblolly Pine

Plantation, USA, Douglas Fir

Plantation (Taungya), Mexico, Spanish cedar

Restoration, Mexico, Pine-oak

Restoration, Southern Africa, Miombo

Agro- forestry, Southern India, Mango- tamarind

Avoidance of deforestation, Mexico,Various

Productivity Medium

Interest Rate 5.83%

Tax Rate 38%

Discount rate 6.09%

Transaction Costs ($/ha) 5

Monitoring/ verification ($/ha/y) 5

Management: Restoration

Country: Mexico

Trees: Pine-oak

Establishment cost ($/ha) 292

Land cost ($/ha) 200

Annual maintenance ($/ha) 60

Maintenance (years) 7

Timber revenue ($/m3) 30

Time Between

Timber yield (m3/ha) : Harvests (yrs)

End of rotation 280 35

Thinning 1

Thinning 2

Forestry: Input Variables
Case 4: Restoration of Degraded Pine-Oak Woodland in Chiapas, Mexico Through Enrichment Planting With 

Pinus oocarpa

14-7

Table 14-5

Forestry summary of results for Pine-Oak medium productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Restoration

Country/Region: Mexico

Trees: Pine-oak

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 20.47

Above & below ground carbon 12.68

Above & below ground & timber product carbon 12.20

Above & below ground carbon & non-CO2 GHGs 13.03

Above ground carbon with timber revenues 3.54

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All )* 2.17

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

Medium Productivity

-50

0

50

100

150

200

0 20 40 60 80 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

-10000

-8000

-6000

-4000

-2000

0

2000

4000

0 20 40 60 80 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 4: Restoration of Degraded Pine-Oak Woodland in Chiapas, Mexico Through Enrichment Planting With 

Pinus oocarpa

14-8

Table 14-6

Forestry summary of results for Pine-Oak low productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Restoration

Country/Region: Mexico

Trees: Pine-oak

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 29.06

Above & below ground carbon 18.32

Above & below ground & timber product carbon 17.55

Above & below ground carbon & non-CO2 GHGs 19.07

Above ground carbon with timber revenues 9.68

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All )* 6.07

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

Low Productivity

0

20

40

60

80

100

120

140

160

0 20 40 60 80 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

-8000

-6000

-4000

-2000

0

2000

4000

0 20 40 60 80 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 4: Restoration of Degraded Pine-Oak Woodland in Chiapas, Mexico Through Enrichment Planting With 

Pinus oocarpa

14-9

Table 14-7

Forestry summary of results for Pine-Oak high productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Restoration

Country/Region: Mexico

Trees: Pine-oak

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 17.77

Above & below ground carbon 10.86

Above & below ground & timber product carbon 10.33

Above & below ground carbon & non-CO2 GHGs 11.11

Above ground carbon with timber revenues (5.00)

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All )* (2.97)

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

High Productivity

-50

0

50

100

150

200

250

0 20 40 60 80 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

-20000

-15000

-10000

-5000

0

5000

0 20 40 60 80 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 4: Restoration of Degraded Pine-Oak Woodland in Chiapas, Mexico Through Enrichment Planting With 

Pinus oocarpa

14-10

14.8 References

Some of these references are not indicated specifically in the body of the chapter and instead 

represent background for the information presented in this chapter.

1 Greaves A. (1982) Pinus oocarpa. [Review Article]. Forestry Abstracts 43(9) 503-526

2 Webb D.B., Wood P.J., Smith J.P. and Henman G.S. (1984) A Guide to Species Selection 

for Tropical and Subtropical Plantations. Tropical Forestry Paper 15, Oxford, UK

3 CABI Forestry Compendium

4 Hellier A., Newton A.C., Ovhoa Gaona S. (1999) Use of indigenous knowledge for rapidly 

assessing trends in biodiversity: a case study from Chiapas, Mexico. Biodiversity and 

Conservation 8: 869-889

5 ECCM technical note for classifying productivity in the absence of yield class information.

6 de Jong per. comm.

7 Montoya G., Soto L., de Jong B., Nelson K., Farias P., Taylor J. and Tipper R. (1995) 

Desarrollo forestall sustenable: captura de carbono en las zones tzeltal y tojolabal del estado 

de Chiapas. Cuadernos de Trabajo 4, Instituto Nacional de Ecología, Mexico
15-1

15

CASE 5: RESTORATION OF DEGRADED DRY MIOMBO 

WOODLAND IN SOUTHERN AFRICA

15.1 Introduction

Miombo woodland is found in central, southern and eastern Africa, and is typified by the 

dominance of the genera Brachystegia, Julbernardia and/or Isoberlinia. It is an ecologically and 

economically important ecosystem, providing a valuable source of timber, fuelwood and game 

for a population of around 40 million people. Large areas of miombo have been seriously 

degraded due to extraction of precious hardwoods, fuelwood and charcoal. The degradation of 

miombo woodland causes severe problems for local populations, for erosion control and 

maintaining water quality. Restoration through enrichment planting with native species 

combined with the development of sustainable agroforestry could provide a sustainable resource 

for local communities and prevent degradation of the environment, as well as provide carbon 

sequestration benefits. In many cases restoration of miombo woodlands will be part of a larger 

community-based programme for agricultural and environmental improvement.

15.2 Ecology

Miombo is the term used to describe woodlands of the south subhumid tropical zone in Africa, 

which are distinguished by the dominance of the genera Brachystegia, Julbernardia and 

Isoberlinia. Miombo occurs across approximately 2.8 million km2 in Southern Africa and 

contains over 8000 species of higher plants. Mean annual rainfall within its range is between 

710-1365 mm with mean temperatures of 18-23°C. In most areas, 95 percent of the rainfall 

occurs in a 5-7 month wet-season. Soils are generally nutrient poor and acidic with low organic 

matter (<3 percent). In more nutrient rich area or areas with higher rainfall and deeper soils, 

miombo woodland grades into other vegetation types. It is thought that the presence of root 

ecto-mycorrhizae possessed by miombo species may allow them to exploit the porous and 

infertile soils. Dry miombo woodland occurs in areas with <1000 mm annual rainfall, such as 

central and northern Mozambique. Dry miombo is less floristically diverse than wet miombo 

and has a lower biomass.

15.3 Management Operations

Typical management operations are described in the following subsections.
Case 5: Restoration of Degraded Dry Miombo Woodland in Southern Africa

15-2

15.3.1 Establishment

Establishment involves planting of nursery-grown seedlings consisting of a mosaic of species, 

possibly supplemented by natural regeneration. Species include Brachystegia spiciformis, 

Julbernadia globiflora, Millettia stuhlmanii, Pterocarpus angolensis, Dhalbergia melanoxylon, 

Afzelia quanzensis, Acacia nigrenscens, Swatzia madascariensis, Combretum imberbe, Khaya 

nyassicca, Albizzia veriscolour, Erthrophleoum suavolens and others. Land is game-fenced and 

firebreaks established. Planting of pioneer species in the initial stages allows quick 

establishment of the woodland.

15.3.2 Maintenance

Fire is an important factor in miombo woodland. Control of fire, particularly of anthropogenic 

origin for hunting or to create new grazing areas, will be an important part of management for 

carbon. Control of herbivores, including damage by elephants, is also important.

15.3.3 Harvesting

Thinnings take place firstly in years 5-7 to produces poles for traditional housing as well as 

fuelwood. Sustainable timber extraction occurs with harvests of around 30m3/ha over a 30-year 

period.

15.3.4 Re-establishment

Natural regeneration of small areas is considered suitable, with planting of nursery-grown 

seedlings where required.

15.4 Classification of Productivity

There is little information on relative productivity of miombo woodlands though it has been 

found that above-ground biomass increases with increasing rainfall. Productivity can range from 

55 t/ha of above ground biomass in old-growth dry miombo woodland to about 90 t/ha in mature 

wet miombo. As discussed above, on deeper, more nutrient rich soils, vegetation will grade into 

evergreen or semi-evergreen forest.

15.5 Carbon Sequestration Potential

15.5.1 Baseline

The following are the initial assumptions for existing stored carbon at the site based on ECCM 

information.

• Vegetation: 6 tC/ha
Case 5: Restoration of Degraded Dry Miombo Woodland in Southern Africa

15-3

• Soils: 5 tC/ha (to 30 cm depth)

15.5.2 Carbon Uptake and Storage in Miombo Woodland

Expected carbon storage is shown in Table 15-1.

Table 15-1

Expected carbon storage from restoration of Miombo woodland over 100 years

Productivity Low Medium High

Rotation length (years) n.a. Continuous 

cover

n.a.

Maximum above ground carbon (tC ha-1) n.a. 43 n.a.

Average above-ground carbon (tC ha-1) n.a. 36 n.a.

Average soil etc. carbon (tC ha-1) n.a. 25 n.a.

15.6 Costs

15.6.1 Establishment and Maintenance

Establishment costs are estimated at approximately $100/ha including nursery establishment and 

training of local staff in forest management and timber utilisation techniques.

Maintenance costs following establishment will be very low. Activities that are carried out, 

including selective harvesting and hunting, will generate small net income or benefits.

15.6.2 Land

The opportunity costs of land associated with restoration of the woodland are zero, since there is 

no change of use and the productivity of the degraded system is virtually zero.

15.6.3 Potential Income

There is potential income from forest products manufactured from sustainable timber harvesting. 

While the growth rates of miombo are low (around 1 m3 per hectare per year) some of the 

species produce very valuable timber (fetching between $500 and $1000/m3 on international 

markets). Other major benefits of restoration of miombo woodland are reduction in soil erosion 

and further environmental degradation, hydrological benefits and provision of a local resource 

for hunting, house building and fuel.
Case 5: Restoration of Degraded Dry Miombo Woodland in Southern Africa

15-4

15.7 Additional Information

It should be noted that most rural populations in the areas where miombo woodland occurs are 

90 percent dependent on fuelwood or charcoal for their energy requirements. Significant 

amounts of wood are exported to towns. If the current rate of miombo degradation and 

deforestation continues then both urban and rural populations will become dependent upon fossil 

fuels (typically kerosene) for domestic energy. The CO2 benefits of avoiding future dependence 

on fossil fuels have not been included in this study.

15.8 Summary of Model Inputs and Results 

A summary of input variables for the base case (medium productivity) Miombo is presented in 

Table 15-2. The cost of CO2 sink enhancement ($/tonne C) is presented on several accounting 

bases in Table 15-3. Cumulative changes in carbon sequestered (tonne C/ha) and costs ($/ha) are 

presented below Table 15-3 for the medium productivity case. Data were unavailable for low 

and high productivity cases for Miombo.

Costs in Table 15-3 were calculated on an NPV basis as described in Chapter 2, and can be 

compared with costs for CO2 capture and storage and other sink enhancement options calculated 

on the same NPV basis.
Case 5: Restoration of Degraded Dry Miombo Woodland in Southern Africa

15-5

Table 15-2

Forestry input variables for Miombo

Plantation, USA, Loblolly Pine

Plantation, USA, Douglas Fir

Plantation (Taungya), Mexico, Spanish cedar

Restoration, Mexico, Pine-oak

Restoration, Southern Africa, Miombo

Agro- forestry, Southern India, Mango- tamarind

Avoidance of deforestation, Mexico,Various

Productivity Medium

Interest Rate 5.83%

Tax Rate 38%

Discount rate 6.09%

Transaction Costs ($/ha) 5

Monitoring/ verification ($/ha/y) 5

Management: Restoration

Country: Southern Africa

Trees: Miombo

Establishment cost ($/ha) 100

Land cost ($/ha) 0

Annual maintenance ($/ha) 40

Maintenance (years) 4

Timber revenue ($/m3) 500

Annually after

Timber yield (m3/ha) : Year

End of rotation

Thinning 1 1 15

Thinning 2

Forestry: Input Variables
Case 5: Restoration of Degraded Dry Miombo Woodland in Southern Africa

15-6

Table 15-3

Forestry summary of results for Miombo medium productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Restoration

Country/Region: Southern Africa

Trees: Miombo

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 22.98

Above & below ground carbon 13.33

Above & below ground & timber product carbon 13.02

Above & below ground carbon & non-CO2 GHGs 14.62

Above ground carbon with timber revenues (139.74)

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All )* (86.65)

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

Medium Productivity

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70 80 90 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

-30000

-25000

-20000

-15000

-10000

-5000

0

5000

0 10 20 30 40 50 60 70 80 90 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 5: Restoration of Degraded Dry Miombo Woodland in Southern Africa

15-7

15.9 References

These references are not indicated specifically in the body of the chapter and instead represent 

background for the information presented in this chapter.

1 Campbell, B. 1996. The Miombo in Transition: Woodlands and Welfare in Africa. Centre 

for International Forestry Research (CIFOR), Bogor, Indonesia.

2 Desanker, P.V. Frost, P.G.H., Frost, C.O., Justice, C.O. and Scholes, R.J. 1997. The 

Miombo Network: Framework for a Terrestrial Transect Study of Land-Use and LandCover Change in the Miombo Ecosystems of Central Africa. IGBP Report 41. The 

Interational Geosphere-Biosphere Programme (IGBP), Stockholm, Sweden.

3 McGhee, W. personal communication.

4 Schwartz, A. personal communication.

5 White, F. 1983. The Vegetation of Africa. Unesco, Switzerland.

16-1

16

CASE 6: AGRO-FORESTRY: MANGO AND NATIVE 

FRUIT ORCHARDS IN SEMI-ARID AREAS OF 

SOUTHERN INDIA

16.1 Introduction

Agroforestry is the combination of tree growing and crop and livestock production within the 

same farming system, such that the total output is greater than could be achieved by the 

implementation of a single component. In the extensive dry-land areas of southern India, such as 

North Kolar District, Karnataka, agro-forestry is a suitable system of land management. Small 

farmers can grow fruit trees and fast growing fuel wood species as wind breaks on water 

collection bunds. Depending on the soil type, soil depth, water availability for the particular 

plot, and management capability of the farming family, various fruit trees may be introduced to 

supplement income and nutrition. This case involves agro-forestry: Mango and native fruit 

orchards (Tamarind, Jackfruit, Jamun, Pongemia) in semi-arid areas of southern India 

16.2 Ecology

Trees are selected on the basis of multiple attributes, including their contribution to water 

conservation, soil fertility, fruit quality, fuelwood production and shade effect. Native fruit 

orchards are a viable option for areas such as the Kolar district of Karnataka in southern India 

(600-1200 masl), where rainfall is highly seasonal, with 80 percent of the annual rainfall of 

800 to 1200 mm falling in a 2-3 month period and where temperatures range from 10oC to 45oC. 

Local varieties of Mango and Tamarind are likely to be the main components of the orchards. 

Other trees such as Jack Fruit, Jamun, Pongemia, Neem, and Acacia auriculiformis may also be 

introduced as windbreak trees for fuelwood, timber and fodder for browsing by small livestock. 

Intercropping with groundnuts or beans is carried out for the first 12 years. These species fit 

well with the existing farming systems, where water management is critical. Villages are 

encouraged to dig ponds and bunds (low mounds and ditches) for catching the 4 - 5 annual flash 

floods which bring up to half the annual rainfall on their plots. Once the ponds dry out, farmers 

become dependent upon bore-wells for water to irrigate their plots. Tree orchards help to reduce 

the runoff and peak stream flows after sudden downpours and the deep root systems that do not 

compete strongly with the staple crops, they also provide out-of-season income from fruits that 

keep families going in the lean months.

Timber extraction and charcoal production have ruined many of the old Tamarind stands in the 

Kolar district. Farm trees are often in poor form due to indiscriminate lopping for fuelwood but 

good examples of individual Mango trees and Tamarind “toops” (stands) may still be found.
Case 6: Agro-forestry: Mango and Native Fruit Orchards in Semi-Arid Areas of Southern India

16-2

16.3 Classification of Productivity

Shown in Table 16-1 are the important parameters for productivity for the chosen species:

Table 16-1

Productivity factors for mixed orchards of Tamarind and Mango

Productivity Conditions

Production

(mango fruit)

High 600-1200m; deep, well-drained loamy soil, good subsoil ; temp 10-38oC, rainfall >1000mm/y, with good 

rains from June to September followed by a more or 

less dry spell. The quantum of rainfall is not as 

important as its distribution.

10 t ha-1 (5 y)

20 t ha-1 (10 y)

30 t ha-1 (20 y)

Medium Intermediate 8 t ha

-1

 (5 y)

15 t ha-1 (10 y)

25 t ha-1 (20 y)

Low Stony soils with temperatures exceeding 40oC for 

prolonged periods, rainfall during flowering and fruitsetting (February – April).

5 t ha-1 (5 y)

10 t ha-1 (10 y)

15 t ha-1 (20 y)

16.4 Management Operations

Typical management operations are described in the following subsections.

16.4.1 Establishment

Planting pits are dug and filled with red earth, sand and manure. Irrigation channels are dug and 

wind-breaks should have been planted one or two years before. Irrigation, training, hoeing, 

weeding and staking to protect against high winds have to be carried out at regular intervals. 

Grafted plants are kept free of branching up to at least 75cm height, with as little pruning as 

possible being done. Inter-cultivation is necessary to control Mango mealy bug in November 

and December. Mangos grow well even in poor soils due to their deep root system but during 

the non-bearing period, N P K fertilizer should be applied.

Between the trees in the centre of the plot, legumes, millet and groundnuts are planted in rows up 

to the 12th year. Thereafter inter-cropping will probably cease. As Tamarind does not allow 

such annual crops to come up under its shade, Tamarind is placed at the edge and the corners of 

the field. The suitable hybrid varieties of Mango are Ratnagiri Alfonso, Benisha, H 13, and 

Mallika. Suitable Hybrid Tamarind varieties are Urigam, and local Red Tamarind varieties. The 
Case 6: Agro-forestry: Mango and Native Fruit Orchards in Semi-Arid Areas of Southern India

16-3

Jack Fruit and Jamun trees are placed suitably in the centre of a hexagon of Mangos, and the Old 

Variety Mangos are planted on the bund between the Neem and Pongemia and left to grow to 

their natural massive height.

220 Mango trees, 20 Tamarind trees, 20 Jack Fruit, 20 Jamun and 20 old variety Mango are 

planted per hectare. Pongemia, Neem, and Acacia Auriculiformis are planted as windbreak trees 

for fuel and fodder at 2.5 m spacing in 2 rows along the bunds.

16.4.2 Harvesting/Replanting

The trees are replaced in the following cycles:

Mango grafted 100% every 40 years

Tamarind 100% every 40 years

Jack 100% every 80 years

Jamun 100% every 30 years

Old Variety Mango 100 years +

Neem, Pongemia and Acacia Auriculiformis will be coppiced for fuel wood, and the leaves and 

twigs cut for mulch.

16.5 Carbon Sequestration Potential

16.5.1 Baseline

The baseline vegetation for most new orchards will be a sparse covering of shrubs and small 

trees. Estimated above-ground carbon density is around 8 tC ha-1. Soil carbon is estimated to be 

20 tC ha-1.

16.5.2 Carbon Uptake and Storage by Orchards

Carbon uptake by the vegetation is constrained by the availability of moisture during much of the 

year and while groves of old Tamarind trees have been estimated to contain over 300 tC ha-1

based on data from the Centre of Ecological Science4.

Mean carbon storage figures for sites of different productivity levels are not yet available. 

However, it is estimated that the typical carbon storage over 100 years from establishment will 

be as shown in Table 16-2.
Case 6: Agro-forestry: Mango and Native Fruit Orchards in Semi-Arid Areas of Southern India

16-4

Table 16-2

Expected carbon storage from Mango-Tamarind agro-forestry over 100 y

Productivity Low Medium High

Rotation length (years) n.a. Continuous

cover

n.a.

Maximum above ground carbon (tC ha-1) n.a. 60 n.a.

Average above-ground carbon (tC ha-1) n.a. 33 n.a.

Average soil etc. carbon (tC ha-1) n.a. 33 n.a.

16.6 Costs

16.6.1 Establishment and Maintenance

It is estimated that the costs of establishment are between $100 and $300 per hectare in the first 

year.

In addition to the establishment, the mango-tamarind orchard will require considerable labor

input, for watering and weed control during the establishment phase. The actual cost will depend 

upon the water requirement of the growing seedlings and the availability / proximity of a reliable 

water supply. For sites where there is an existing network of bunds, and water retention 

facilities, the maintenance costs during the first 5 years will be $100 to $150 per hectare per year, 

depending on the availability of labor and inputs. In much of southern India the price of farm 

labor is extremely low – less than $1 per day.

Where the construction of water tanks, boreholes or other water retaining features are required 

the initial establishment costs could be significantly higher - in the order of $1000 per hectare per 

year during the first 5 years. However, for the farmers / communities this investment will also 

see a considerable increase in the value of the land.

16.6.2 Land

The main alternative land use will be cultivation for groundnuts. Yields are sporadic, depending 

upon the rainfall and the returns per hectare will range from almost zero to $150/year. On 

improved land (with bunds for water retention and fertilizer input) the returns will be higher. An 

annual opportunity cost of $60 to $120 per hectare was selected. This corresponds to a land cost 

of about $900 per hectare (ten times the average opportunity cost) at establishment.

16.6.3 Potential Income

The system is managed primarily for fruit, with mango being the main cash crop. A mixed 

orchard will produce $200 to $600 per hectare depending on age, productivity class and the 
Case 6: Agro-forestry: Mango and Native Fruit Orchards in Semi-Arid Areas of Southern India

16-5

mixture of species. Inter-cropped legumes and groundnuts are also harvested for the first 

10 years, or so. Fuelwood, fodder, and some fence-posts are assumed not to make a substantial 

difference to the total standing biomass or the carbon flows of the plot.

Other products from the orchards will include fodder from the leaves of Neem and cooking oils 

for Pongemia and Neem seeds.

16.7 Additional Comments

While the carbon uptake and storage potential of forestry / agroforestry systems in semi-arid 

conditions is relatively low, the costs of establishment and the associated benefits in terms of 

long term benefits to the rural economy make this an attractive option.

The carbon benefits of these systems can be further enhanced by utilising woody biomass for 

electricity generation. Many villages in the region referred to are dependent upon electrical or 

diesel pumps for extracting water from bore-wells. A number of promising examples of biofuelled generators and water pumps have been established.

16.8 Summary of Model Inputs and ResultsA summary of input variables for the base case (medium productivity) Mango-tamarind is 

presented in Table 16-3. The cost of CO2 sink enhancement ($/tonne C) is presented on several 

accounting bases in Table 16-4. Cumulative changes in carbon sequestered (tonne C/ha) and 

costs ($/ha) are presented below Table 16-4 for the medium productivity case. Data were 

unavailable for low and high productivity cases for Mango-tamarind. Costs in Table 16-4 were 

calculated on an NPV basis as described in Chapter 2, and can be compared with costs for CO2

capture and storage and other sink enhancement options calculated on the same NPV basis.
Case 6: Agro-forestry: Mango and Native Fruit Orchards in Semi-Arid Areas of Southern India

16-6

Table 16-3

Forestry input variables for Mango-Tamarind

Plantation, USA, Loblolly Pine

Plantation, USA, Douglas Fir

Plantation (Taungya), Mexico, Spanish cedar

Restoration, Mexico, Pine-oak

Restoration, Southern Africa, Miombo

Agro- forestry, Southern India, Mango- tamarind

Avoidance of deforestation, Mexico,Various

Productivity Medium

Interest Rate 5.83%

Tax Rate 38%

Discount rate 6.09%

Transaction Costs ($/ha) 5

Monitoring/ verification ($/ha/y) 5

Management: Agro- forestry

Country: Southern India

Trees: Mango- tamarind

Establishment cost ($/ha) 200

Land cost ($/ha) 900

Annual maintenance ($/ha) 125

Maintenance (years) 5

Timber revenue ($/m3) 400 $/y fruit

Timber yield (m3/ha) :

End of rotation

Thinning 1

Thinning 2

Forestry: Input Variables
Case 6: Agro-forestry: Mango and Native Fruit Orchards in Semi-Arid Areas of Southern India

16-7

Table 16-4

Forestry summary of results for Mango-Tamarind medium productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Agro- forestry

Country/Region: Southern India

Trees: Mango- tamarind

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 176.93

Above & below ground carbon 129.00

Above & below ground & timber product carbon 129.00

Above & below ground carbon & non-CO2 GHGs 154.10

Above ground carbon with timber revenues (181.20)

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All)* (157.82)

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

Medium Productivity

-10

0

10

20

30

40

50

60

70

80

90

0 10 20 30 40 50 60 70 80 90 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

-25000

-20000

-15000

-10000

-5000

0

5000

0 10 20 30 40 50 60 70 80 90 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 6: Agro-forestry: Mango and Native Fruit Orchards in Semi-Arid Areas of Southern India

16-8

16.9 References / Information Sources

Some of these references are not indicated specifically in the body of the chapter and instead 

represent background for the information presented in this chapter.

1 Agro-ecological information was compiled from farmers in the North Kolar District, 

Karnataka.

2 Economics of establishment: Anandi Sharan of WSD, Bangalore.

3 Management of mango plantations: Fruit Growing, J.S. Bal, Kalyani Publ., New Delhi, 

1997.

4 Carbon uptake estimates supplied by Dr. Nagraj of Centre of Ecological Science, Bangalore 

2000.
17-1

17

CASE 7: AVOIDANCE OF DEFORESTATION IN 

CHIAPAS, MEXICO

17.1 Introduction

Deforestation, particularly in tropical regions, is one of the largest sources of anthropogenic 

greenhouse gas emissions. Carbon emissions from deforestation in the tropics during the 1990s 

range from 1.1 to 1.7 GtC/y, with a best estimate of 1.6 GtC/y1. In tropical countries with large 

areas of remaining forest, reduction of deforestation has much greater potential climate benefits 

than other forestry options. Reduction of deforestation also usually produces more 

complementary benefits, such as maintaining biodiversity. On the other hand, quantifying the 

direct benefits of a programme of avoiding deforestation is much more difficult than quantifying 

the direct effects of more discrete activities.

The rates and causes of deforestation vary by region and scale. Conversion of forests to pasture 

and cropland has been the most direct cause of tropical deforestation. Non-sustainable logging 

has been the leading factor in parts of South-east Asia, whereas excessive harvest of wood fuel 

has been important only in specific sub-country regions and in some African countries.

The avoidance of deforestation cases in this study are based on information from the Chiapas 

region of southern Mexico, although conditions are similar is many other Central and South 

American countries.

17.2 Ecology

Three types of forest, which account for a large proportion of the existing native forest in 

Chiapas, are included in this case:

• Pine-oak forest 

• Moist tropical forest

• Montane cloud forest

Pine-oak forest occurs naturally in temperate to sub-tropical mountain regions. Further details 

are given in Chapter 14.

Moist tropical forests consist of communities that reach up to 30m high and contain a great 

diversity of canopy species. They occur mainly at altitudes of <500 m and rainfall of 

>2000 mm/y.
Case 7: Avoidance of Deforestation in Chiapas, Mexico

17-2

The canopy of the mountain forest is dominated by various species of oak and can reach up to 

40m high. It occurs at altitudes of >1500 m and rainfall of >1000 mm/y.

The forests of Chiapas exhibit important characteristics in common with many regions of Latin 

America. The Altos or Central Highlands displays land use patterns that can be recognized in 

highland settings in Guatemala, Peru, Colombia and Ecuador as well as other areas of Mexico. 

Such areas contain established, traditionally managed agricultural systems and high population 

densities. The Lowland region, in common with many areas of extensive moist tropical forest in 

Latin America, is subject to rapid colonization, both by subsistence farmers and ranchers.

17.3 Carbon Sequestration Potential

The carbon sequestration potential depends on the expected rate of deforestation in the baseline, 

i.e., without any additional forest preservation measures. There are a number of issues 

associated with setting baselines for projects that purport to avoid emissions by preventing 

deforestation. The key issues are:

• How can project developers be prevented from skewing the results in their favor

(overestimating the probable loss of forest)?

• How can an objective measurement of the risk of emissions be derived?

• How can we the error associated with predicted emissions be measured?

• How can a consistent methodology be applied to different projects within the same region?

In response to these questions, the Edinburgh Centre for Carbon Management (ECCM) has 

developed a standardized, objective method for setting baselines for deforestation prevention 

projects, using a risk-assessment approach, which relates the probability of future loss of 

terrestrial carbon to key driving factors that exist within a particular region2. This method, called 

the Regional Risk Matrix (RRM) method, produces a baseline map that allows any project within 

the region studied to be given an indicative figure for % loss of above-ground carbon over a 

specific (approx. 20 year) period into the future. A simplified version of this method is used in 

this study.

The RRM method consists of the following steps that were applied, to a study area of 2.8 million 

ha in southern Mexico by ECCM and El Colegio de la Frontera Sur:

1. A series of satellite images of the area, showing vegetation changes over a 20 year period 

were analyzed: around 15 types of vegetation were identified and mapped, and changes in 

vegetation cover ove r a 20 years were calculated and mapped.

2. Estimates of above-ground biomass for each vegetation type were combined with the land 

use change data to provide estimates of emissions over a 20-year period.

3. Maps of agricultural land use, roads and population densities were digitized for the study 

area and were compared with the maps of land use change.
Case 7: Avoidance of Deforestation in Chiapas, Mexico

17-3

4. A number of probability matrices describing the relationship between emissions per unit of 

above ground biomass and key “driving and predisposing” factors of land use change were 

derived.

5. These matrices were then used to drive a model of future emissions, based upon the 

assumption that the land use change processes remain similar. This produced a map of 

probable future emissions for the next 20 years.

6. The prediction errors of different matrices were measured by comparison against the map of 

historical emissions (the predicted map was overlain upon the historic map to give a spatially 

explicit measure of the errors).

Further refinement of the RRM is undertaken in iterations, involving:

7. Division and re-calibration of the matrix for sub-regions, as appropriate to reduce prediction 

error.

Two of the main driving factors for deforestation are the population density and the distance 

from a road. Figure 17-1 shows the percentage loss of above ground carbon in 2.8 million ha of 

Chiapas, Mexico between 1976 and 1996 and Figure 17-2 shows the population densities and 

distances from roads. The emissions were derived from a series of vegetation maps and biomass 

estimates. Total emissions during this period were approximately 140 million tC.
Case 7: Avoidance of Deforestation in Chiapas, Mexico

17-4

Figure 17-1

Emissions from land use change in Chiapas, Mexico, over 20 years from the mid-1970’s.
Case 7: Avoidance of Deforestation in Chiapas, Mexico

17-5

Figure 17-2

Map of two key causal factors in land-use change: Distance to roads and tracks and 

population density

Note that the red area to the west of the map corresponds to the densely populated central 

highlands and the green area to the east corresponds to the lowland tropical forests.

The relationship between the quantity of above ground carbon lost between 1976 and 1996 and 

population density and distance from roads and tracks is summarized in Table 17-1.

Table 17-1

Percentage of above-ground carbon lost from 1976 to 1996.

Population density Distance to road (m)

(person/km2) 500 500-1500 1500

>40 63 55 41

20-40 56 46 37

0-20 46 35 25

0 41 28 8
Case 7: Avoidance of Deforestation in Chiapas, Mexico

17-6

Predictions of the quantities of carbon that are expected to be lost over the next 20 years for the 

2.8 million ha study area are shown in Figure 17-3. The expected emissions over this period are 

around 90 million tC. The quantities of carbon lost range from 12 to 150 tC/ha. The current 

matrix models are able to provide baselines with an average site-specific error of +/- 20 percent 

and an aggregate error for the region as a whole of +/- 5 percent.

Figure 17-3

Regional baseline for 2000 to 2020, showing expected emissions tc/ha

For the purposes of this study a simplified version of the methodology derived by ECCM has 

been used. The rate of deforestation is assumed to depend on three factors:

• Base rate

• Vulnerability

• Pressure

Pressure is linked to the population density and vulnerability is linked to the distance to roads 

and tracks. Each of these parameters can be defined as low, medium or high and the model 

predicts an annual rate of deforestation. The predicted rates of deforestation are shown in 

Table 17-2
Case 7: Avoidance of Deforestation in Chiapas, Mexico

17-7

Table 17-2

Predicted annual rate of deforestation (%).

Base rate Population Vulnerability

pressure

Low Medium High

Low Low 0.39 0.6 0.84

Medium 0.65 1.00 1.40

High 0.84 1.30 1.82

Medium Low 1.09 1.68 2.35

Medium 1.82 2.80 3.92

High 2.37 3.64 5.10

High Low 1.37 2.10 2.94

Medium 2.27 3.50 4.90

High 2.96 4.55 6.37

The baseline quantity of carbon stored depends on the type of forest; 180 t/ha for pine-oak forest, 

240 t/ha for moist tropical forest, and 350 t/ha for montane cloud forest.

For this study it is assumed that the rate of deforestation will be constant over the next 100 years 

but it should be recognised that over such long time spans the performances of projects that 

avoid deforestation are subject to greater uncertainty than afforestation projects. The benefits of 

afforestation projects depend on the rate of carbon accumulation, which can be predicted with 

reasonable accuracy provided the forest continues to be maintained. The benefits of projects that 

avoid deforestation depend mainly on the baseline rate of deforestation, which is very uncertain 

more than 20 years into the future.

17.4 Costs

17.4.1 Establishment and Maintenance

A large proportion of the forests in Chiapas are legally held by communities rather than 

individuals or the public sector. Any management regime needs approval and consensus at the 

local level if it is to succeed. The total cost of establishment, including the socio-technical costs 

associated with obtaining local consensus, would nevertheless be relatively low and is estimated 

to be $270/ha. An additional allowance of $25/ha for the first four years is also included.

17.4.2 Land Costs

Accessible pine-oak and mountain forests would have substantial opportunity costs due to the 

value of charcoal and bromeliads that are currently extracted at significantly higher rates than the 
Case 7: Avoidance of Deforestation in Chiapas, Mexico

17-8

apparent level of sustainable yield. Some areas of the rainforests would have a relatively high 

opportunity costs associated with the available stocks of high value hardwoods, particularly 

mahogany. The annual opportunity cost is estimated to be about $300/ha.

17.4.3 Potential Income

Some products for local use such as bromeliads, firewood and tree bark could be extracted on a 

sustainable basis from preserved forests. As in the other cases in this study, it is assumed that 

these minor products for local use would not be taken into account in the economic evaluation.

17.5 Summary of Model Inputs and Results 

A summary of input variables for the base case (moist tropical forest, medium vulnerability, 

population pressure and medium productivity) avoidance of deforestation is presented in Table 

17-3. The cost of CO2 sink enhancement ($/tonne C) is presented on several accounting bases in 

Table 17-4. Cumulative changes in carbon sequestered (tonne C/ha) and costs ($/ha) are 

presented below Table 17-4. Costs in Table 17-4 were calculated on an NPV basis as described 

in Chapter 2, and can be compared with costs for CO2 capture and storage and other sink 

enhancement options calculated on the same NPV basis.
Case 7: Avoidance of Deforestation in Chiapas, Mexico

17-9

Table 17-3

Forestry input values for avoidance of deforestation

Plantation, USA, Loblolly Pine

Plantation, USA, Douglas Fir

Plantation (Taungya), Mexico, Spanish cedar

Restoration, Mexico, Pine-oak

Restoration, Southern Africa, Miombo

Agro- forestry, Southern India, Mango- tamarind

Avoidance of deforestation, Mexico,Various

Productivity Medium Base case carbon (tC/ha) 240

Interest Rate 5.83% Annual rate of deforestation 2.8%

Tax Rate 38%

Discount rate 6.09%

Transaction Costs ($/ha) 5

Monitoring/ verification ($/ha/y) 5

Management: Avoidance of deforestation

Country: Mexico

Trees: Various

Establishment cost ($/ha) 270

Land cost ($/ha) 300

Annual maintenance ($/ha) 25

Maintenance (years) 4

Timber revenue ($/m3)

Timber yield (m3/ha) :

End of rotation

Thinning 1

Thinning 2

Forestry: Input Variables
Case 7: Avoidance of Deforestation in Chiapas, Mexico

17-10

Table 17-4

Forestry summary of results for Avoidance of Deforestation medium productivity case

CO2 Sink Enhancement, NPV Basis (years 1-100)

Management: Avoidance of deforestation

Country/Region: Mexico

Trees: Various

Carbon accounting basis ($/tonne C Equivalent)

Above ground carbon 9.68

Above & below ground carbon 9.68

Above & below ground & timber product carbon 9.68

Above & below ground carbon & non-CO2 GHGs 9.87

Above ground carbon with timber revenues 9.68

Above & below ground carbon, non-CO2 GHGs, with timber products & revenues (All)* 9.87

* C Equivalent LC GHG Avoided Basis

Forestry: Summary of Results (Single System)

Medium Productivity

0

50

100

150

200

250

0 10 20 30 40 50 60 70 80 90 100

Years

Carbon Sequestered (tonne C/ha)

Undiscounted Above Ground C Discounted Above Ground C

Undiscounted Above & Below Ground C Discounted Above & Below Ground C

Undiscounted All (C & Non-CO2 GHGs) Discounted All (C & Non-CO2 GHGs)

0

200

400

600

800

1000

1200

1400

1600

1800

0 10 20 30 40 50 60 70 80 90 100

Years

Cost or Net Cost ($/ha)

Undiscounted Net Cost Discounted Net Cost

Undiscounted Cost Discounted Cost
Case 7: Avoidance of Deforestation in Chiapas, Mexico

17-11

17.6 References

1 IPCC (Intergovernmental Panel on Climate Change), Climate change 2001, the scientific 

basis, published by Cambridge University Press, 2001, ISBN 0 521 80767

2 Edinburgh Centre for Carbon Management, 2001, forthcoming paper.

18-1

18

ECONOMICS OF CO2 SINK ENHANCEMENT VIA 

REDUCING TILLAGE ON U.S. CROPLAND—BASIS 

AND APPROACH

18.1 Introduction

Reducing the amount of tillage and associated improvements in residue management beyond that 

already achieved account for about one-half of the potential (75-208 million tonnes of 

carbon/year) for CO2 sink enhancement in U.S. croplands1. The CO2 sink enhancement in 

croplands is the result of increased carbon storage in soil organic matter. The remaining one-half 

of the potential for CO2 sink enhancement on U.S. croplands is highly fragmented and beyond 

the scope and resources of this project.

The main purpose of the cropland component of this project is to estimate, from an energy 

company perspective, the cost of GHG emission offsets ($/tonne carbon equivalent GHG 

avoided) from reducing tillage on U.S. croplands. This involves estimating the following aspects 

of reducing tillage over time: (1) levels of CO2 sink enhancement, (2) changes in GHG 

emissions other than from CO2 sink enhancement (e.g., from fuel use for tillage operations and 

from nitrous oxide emissions), (3) adoption incentives a company would have to pay to get 

farmers to adopt reduced-tillage practices, (4) transaction costs for aggregating and brokering 

GHG credits, and (5) monitoring costs for assuring that contractual obligations are fulfilled. A 

second purpose of this section is to estimate the hectares of reduced-tillage required to offset 

specified quantities of power plant GHG emissions.

18.2 Tillage-System Definitions

Research, development, demonstration, and deployment of reduced-tillage systems has been a 

major agricultural initiative over roughly the last forty years. Significant levels of adoption 

began to occur in the 1970’s. In addition to CO2 sink enhancement, there are many benefits of 

reducing tillage such as fuel, labor, and equipment savings, improved soil quality, and reduced 

soil erosion by both water and wind2. Reductions in soil erosion are highly correlated with the 

percent of the soil surface covered by crop residues at the time of planting. For this reason, 

reduced-tillage systems are commonly characterized by the percent of the soil surface covered by 

crop residues at the time of planting and are commonly called conservation-tillage systems.

Tillage-system definitions published by the Conservation Tillage Information Center3 are used in 

this report. These definitions are as follows:
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-2

Conservation Tillage Types (30 percent or more crop residue left on the soil surface after 

planting):

These types include any tillage and planting system that covers 30 percent or more of the soil 

surface with crop residue, after planting, to reduce soil erosion by water. Where soil erosion by 

wind is the primary concern, any system that maintains at least 1,000 pounds per acre of flat, 

small grain residue equivalent on the surface throughout the critical wind erosion period.

No-till/strip-till—The soil is left undisturbed from harvest to planting except for strips up to 1/3 

of the row width (strips may involve only residue disturbance or may include soil disturbance). 

Planting or drilling is accomplished using disc openers, coulter(s), row cleaners, in-row chisels 

or roto-tillers. Weed control is accomplished primarily with crop protection products 

(herbicides). Cultivation may be used for emergency weed control. Other common terms used 

to describe no-till include direct seeding, slot planting, zero-till, row-till, and slot-till.

Ridge-till—The soil is left undisturbed from harvest to planting except for strips up to 1/3 of the 

row width. Planting is completed on the ridge and usually involves the removal of the top of the 

ridge. Planting is completed with sweeps, disk openers, coulters, or row cleaners. Residue is left 

on the surface between ridges. Weed control is accomplished with crop protection products 

(frequently using banded application) and/or cultivation. Ridges are rebuilt during row 

cultivation.

Mulch-till—Full-width tillage involving one or more tillage trips which disturb all of the soil 

surface and is done prior to and/or during planting. Tillage tools such as chisels, field 

cultivators, disks, sweeps or blades are used. Weed control is accomplished with crop protection 

products and/or cultivation.

Other Tillage Types:

Reduced-till (15-30 percent residue)—Full-width tillage involving one or more tillage trips 

which disturb all of the soil surface and is performed prior to and/or during planting. There is 

15-30 percent residue cover after planting or 500 to 1,000 pounds per acre of small grain residue 

equivalent throughout the critical wind erosion period. Weed control is accomplished with crop 

protection products and/or row cultivation.

Conventional-till or intensive-till—Full-width tillage which disturbs all of the soil surface and 

is performed prior to and/or during planting. There is less than 15 percent residue cover after 

planting, or less than 500 pounds per acre of small grain residue equivalent throughout the 

critical wind erosion period. Generally involves plowing or intensive (numerous) tillage trips. 

Weed control is accomplished with crop protection products and/or row cultivation.

The case studies reported later in this report rely heavily on county- and state-level estimates of 

carbon stored in soils and crop residues as affected by tillage system. These estimates have been 

published for Iowa4, Indiana5, and Nebraska6 for intensive-till, moderate-till, and no-till systems. 

In these reports, moderate-till includes mulch-till and ridge-till systems and no-till systems with 

intermittent intensive-tillage.
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-3

18.3 Effects of Tillage-System on the Amount of Carbon Stored in Soil 

Organic Matter

The production of crop residues (crop stalks and roots) removes CO2 from the atmosphere and 

incorporates the CO2 into crop residues via photosynthesis (Figure 18-1). As the crop residues 

decompose, they release CO2 back into the atmosphere. When crop residues decompose at a 

slower rate than they are added to the soil, the amount of soil organic matter increases and the 

amount of carbon stored in soil organic matter increases proportionally. A key effect of reducing 

tillage is that crop residues decompose more slowly but the amount of crop residues added to the 

soil remains about the same. This results in an increase in the amount of carbon stored in soil 

organic matter. In addition to increasing the soil organic matter of non-eroded soil, 

conservation-tillage systems also significantly reduce the potential for soil erosion and loss of 

CO2 from eroded soil.

Conservation-

 Tillage System

& Residue 

Management

Harvested

 Biomass 

Crop

Residue

Carbon

Production

Soil Organic

Matter

Eroded

Soil CO2

CO2 CO2

Figure 18-1

Mechanisms by which conservation-tillage systems affect the amount of carbon that 

remains stored in soil organic matter

Reducing tillage slows the rate of soil organic matter decomposition for several reasons7. One 

key reason is that the soil tends to be more aggregated and soil organic matter contained inside 

the aggregates is less exposed to soil oxygen, slowing the rate of organic matter decomposition. 

Another key reason is that crop residues on the soil surface reflect sunlight and result in 

significantly lower soil temperatures, further slowing the rate of organic matter decomposition. 

Also, less mixing of crop residues with the soil reduces exposure of the residues to soil moisture 

and nutrients required for rapid decomposition.

After switching from intensive- to conservation-tillage, the increase in soil organic matter 

becomes smaller each year and soil organic matter levels eventually build up to a point that the 

rate of organic matter decomposition equals the rate of crop residue addition. Once this new 

steady-state is reached, no additional carbon is stored in soil organic matter as a result of 
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-4

reducing tillage. An example of reaching a new steady-state level of soil organic carbon 

(?SOCs) is illustrated in Figure 18-2. The data points for 0, 10, and 20 years are model 

predictions from the Iowa Carbon Storage Project4 and the response curve is from a best-fit 

quadratic curve for the 1, 10, and 20-year data points. In this example, the predicted number of 

years (Ys) to reach the new steady-state level of SOC was 29. Results from the Iowa and 

Indiana Carbon Storage Projects indicate that when intensive-till systems are switched to no-till, 

Ys in these states will typically be between 25 and 35 years.

Intensive-till to No-till--Buchanan Co., IA

Corn/Soybean Rotation

0

2000

4000

6000

8000

10000

12000

0 5 10 15 20 25 30 35

Years after switching tillage

?SOC, kg/ha

Ys

? SOCs

Figure 18-2

Increases in soil organic carbon (SOC) via switching from intensive-till to no-till

The native vegetation for U.S. cropland was primarily grass in the drier areas and trees in the 

more humid areas. Soil organic matter levels were much higher under native vegetation than 

they are today after several decades of cultivation. When the grasslands and forests were 

converted to cropland with intensive tillage systems, soil organic matter levels declined steadily 

until a new steady-state level was reached. The general decline in SOC for the central Corn Belt 

is illustrated in Figure 18-3

8

. From the early 1900’s until about 1950, SOC levels declined 

steadily and then remained at a new steady-state level until about 1970. With the advent of 

conservation-tillage practices in the 1970’s, the general SOC levels in the central Corn Belt 

began to rise. Further adoption of conservation-tillage practices will help to restore part of the 

SOC that existed under natural vegetation.
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-5

Figure 18-3

Trends in soil organic carbon (SOC) in the central corn belt 

18.4 Regions With Greatest Potential for Increasing Soil Organic Carbon

Regions with the greatest potential for increasing SOC via reducing tillage are regions with large 

amounts of cropland and large per hectare increases in SOC as a result of reducing tillage. 

Counties with 20,000 or more hectares of cropland are presented in Figure 18-4

9

. These counties 

meet the criteria of relatively large amounts of cropland. Most of this cropland is east of the 

Rocky Mountains. Generally, SOC levels increase moving east from the Rocky Mountains 

through the Great Plains and into the Corn Belt, mainly because of increasing precipitation levels 

and the associated increase in crop production and amount of crop residues added to the soil7.

Likewise, the potential per hectare increases in SOC that can occur as a result of reducing tillage 

will be greater moving east from the Rocky Mountains through the Great Plains and into the 

Corn Belt, mainly because of increasing precipitation levels and the associated increase in crop 

production and amount of crop residues added to the soil.
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-6

Figure 18-4

Counties with more than 20,000 hectares of cropland

The region comprising roughly the western third of the Great Plains states of North Dakota, 

South Dakota, Nebraska, Kansas, Oklahoma, and Texas and counties on west to the Rocky 

Mountains produces predominantly dry land small grains (mainly wheat). Crop production 

levels and associated residue production levels in this region are severely limited by the amount 

of rainfall. The traditional cropping system in this region is a wheat/fallow rotation. A crop is 

grown every two years and the land is left fallow in the alternate years to store soil moisture for 

the next crop. In traditional wheat-fallow systems in this region, crop residues are added back to 

the soil only every other year and soil organic matter levels are continuing to decline from levels 

under native vegetation. Switching to no-till wheat/fallow maintains current soil organic matter 

levels but does not result in a significant increase in soil organic matter. These relationships are 

presented for an eastern Colorado location in Figure 18-5 which is based on model simulations10. 

Switching to no-till in the traditional wheat/fallow region holds promise for conserving enough 

additional soil moisture to enable growing two crops out of three years or three crops out of four 

years, in which case moderate additional increases in soil organic matter levels can be achieved 

over that with no-till wheat/fallow11.
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-7

Figure 18-5

Simulated changes in soil organic carbon (SOC) as affected by tillage and cropping 

systems in eastern Colorado

The silage corn, corn, and corn/alfalfa curves in Figure 18-5 are for irrigated, conventionallytilled systems in which crop production is not limited by moisture. The large increase in SOC in 

these cropping systems compared to the curves for dryland wheat illustrates the extremely 

important impact of crop productivity and crop residue production levels on SOC levels. Even 

greater increases in SOC could be achieved in the irrigated systems by using conservation tillage 

instead of conventional tillage.

Roughly the eastern two-thirds of the Great Plains states mentioned above receives enough 

precipitation to enable growing a crop every year. Therefore, crop residues are added to the soil 

each year and this region has the potential to achieve greater increases in soil organic matter via 

reducing tillage than does the region discussed in the previous paragraph.

The Corn Belt states of Iowa, Illinois, Indiana, Ohio, Minnesota, Wisconsin, Michigan, and 

Missouri receive more rainfall than the Great Plains states and produce significantly more crop 

residues/hectare than the Great Plains. Most of the cropland in these states is in a corn/soybean 

rotation. In these states, there is a slightly larger area in corn than soybeans. Most of the 

“excess” corn area is in continuous corn or is rotated with crops in addition to soybeans (mainly 

wheat). Typical increases in SOC due to switching from intensive-till to no-till at a central 

Indiana location are presented in Figure 18-6

10. Based on acreage and SOC increases per 

hectare, Corn/soybean rotations in the Corn Belt offer the greatest potential in the United States 

for increasing SOC via reducing tillage.
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-8

Figure 18-6

Simulated changes in soil organic carbon (SOC) as affected by tillage and cropping 

systems in central Indiana

Counties with more than 20,000 hectares in the Mississippi corridor to the south of the Corn Belt 

(Figure 18-4) also have significant potential for increasing SOC via reducing tillage. Cotton and 

soybeans are major crops in these counties. Crop productivity and amounts of crop residues 

added to the soil tend to be lower than in the Corn Belt and, due to higher temperatures, the rate 

of crop residue decomposition tends to be higher than in the Corn Belt. These factors result in 

less potential on a per hectare basis for increasing SOC via reducing tillage than occurs in the 

Corn Belt.

18.5 Current Levels of Conservation-Tillage Adoption

18.5.1 Corn and Soybeans in the Corn Belt

The hectares of corn and soybeans and adoption of conservation-tillage in a north-south transect 

in the eastern Corn Belt are presented in Table 18-1

12. Two trends are evident in Table 18-1. 

First, conservation-tillage adoption levels are much higher for soybeans than corn. Adoption 

levels are higher for soybeans than corn mainly because soybeans are better adapted to 

conservation-tillage and pose less risk of reduced income to the farmer. Since almost all 

soybeans are rotated with corn, Table 18-1 implies that there is a significant amount of the 

cropland in which soybeans are in conservation-tillage and corn is in intensive tillage. For 

example, in Indiana, 59+15=74 percent of the full season soybeans are in conservation tillage 

and 21+8=29 percent of the corn is conservation-tillage. This implies that roughly 45 percent 
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-9

(74-29 percent) of the land in conservation-till soybeans is not in conservation-till corn. The 

second trend is that conservation-tillage adoption levels increase going from north to south. One 

of the challenges of conservation tillage is that leaving more crop residues on the soil surface 

causes soils to warm and dry more slowly in the spring. This can delay planting in the spring or 

at least slow growth in the spring and increase the risk of yield reductions with conservation 

tillage. The risk reduces going from north to south as reflected by the conservation-tillage 

adoption levels.

Table 18-1

Adoption of conservation-tillage practices in the eastern corn belt and mid-south U.S.

Conservation-till Intensive -till

No-till Mulch-till 15-30% 0-15%

State Crop M hectares % of hectares

MI Corn (FS) 1.1 14 12 20 54

MI SB (FS) 0.8 39 9 16 36

IN Corn (FS) 2.3 21 8 19 53

IN SB (FS) 2.2 59 15 11 15

IN SB (DC) 0.1 80 13 2 5

KY Corn (FS) 0.5 56 16 12 16

KY SB (FS) 0.3 56 15 11 19

KY SB (DC) 0.2 86 11 2 1

TN Corn (FS) 0.2 54 11 16 19

TN SB (FS) 0.3 50 7 18 25

TN SB (DC) 0.1 85 5 5 5

FS=full season; DC=double crop

The hectares of corn and soybeans and adoption of conservation-tillage in a north-south transect 

in the western Corn Belt are presented in Table 18-2

12. The same trends are evident as were 

discussed above for Table 18-1.
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-10

Table 18-2

Adoption of conservation-tillage practices in the western corn belt

Conservation-till Intensive -till

No-till Mulch-till 15-30% 0-15%

State Crop M hectares % of hectares

MN Corn (FS) 2.9 2 23 1 74

MN SB (FS) 2.8 4 42 2 51

IA Corn (FS) 4.8 18 27 34 21

IA SB (FS) 4.3 27 50 16 7

MO Corn (FS) 1.1 24 11 24 41

MO SB (FS) 1.9 37 16 17 30

MO SB (DC) 0.2 58 9 11 21

FS=full season; DC=double crop

18.5.2 Small Grains

Adoption of conservation-tillage practices in the major small grain producing states in year 2000 

is presented in Table 18-3

12. A majority of the small grains are still intensively tilled. In the 

traditional stubble-mulch wheat-fallow systems, seven or more tillage operations are performed 

during the fallow period between wheat crops. Sweep undercutters and rod weeders are 

commonly used in these systems. These implements don’t invert the soil and leave more crop 

residues on the soil surface than disks or chisel plows, but after seven or more operations during 

the fallow period, less than 30 percent residue cover is left in most of the traditional wheatfallow systems.
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-11

Table 18-3

Adoption of conse rvation-tillage practices in major small grain states in year 2000

Conservation-till Intensive -till

No-till Mulch-till 15-30% 0-15%

State Crop M hectares % of hectares

WA SSSG 0.4 17 20 34 29

FSSG 0.8 7 19 40 34

MT SSSG 3.4 9 34 32 25

FSSM 0.8 15 38 32 14

ND SSSG 5.4 10 16 28 43

FSSG 0.6 8 10 22 60

SD SSSG 0.8 16 15 34 35

FSSG 0.6 19 10 42 29

NE FSSG 0.6 7 14 18 60

KS FSSG 4.4 6 15 25 53

CO FSSG 1.5 4 35 28 34

OK FSSG 2.4 5 18 25 51

TX FSSG 2.7 3 28 26 42

SSSG=spring seeded small grains; FSSG=fall seeded small grains.

18.5.3 Cotton

Adoption of conservation-tillage practices in the major cotton producing states in year 2000 is 

presented in Table 18-4

12. Until recently, adoption of conservation-tillage in cotton has been 

slow. With the advent of herbicide resistant cotton, adoption of no-till cotton has occurred more 

rapidly in some states such as Alabama and Tennessee.
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-12

Table 18-4

Adoption of conservation-p in major cotton states in year 2000

Conservation-till Intensive -till

No-till Mulch-till 15-30% 0-15%

State Crop M hectares % of hectares

MO Cotton 0.1 5 3 3 66

AK Cotton 0.4 4 1 7 84

LA Cotton 4.8 18 27 34 21

TN Cotton 0.2 45 1 7 47

MS Cotton 0.8 7 4 18 70

AL Cotton 0.2 35 4 2 11

TX Cotton 2.8 1 2 10 86

18.6 GHG Effects in Addition to Carbon Storage in Soil Organic Matter

Reducing tillage has several important effects on GHG emissions in addition to increasing the 

amount of carbon stored in soil organic matter. These include changes in GHG emissions from 

the following:

• Fuel use for field operations

• Machinery manufacturing, transportation, and repair

• Nitrogen (N) fertilizer use

• Herbicide use

• Nitrous oxide (N2O) emissions from soil

These changes in GHG emissions due to reducing tillage continue indefinitely, even after a 

steady-state level of SOC has been attained. The net effect of the changes in these emissions and 

the increase in SOC due to reducing tillage is referred to in the case studies as avoided GHG 

emissions. Avoided GHG emissions will be estimated for a 100-year planning horizon. An 

overview of factors affecting GHG emissions by means other than carbon storage in SOM is 

presented below. More details are provided in the case studies.

18.6.1 Fuel Use

Reducing the number and intensity of tillage operations reduces fuel use and the associated CO2

emitted from fuel use.
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-13

18.6.2 Machinery Required

Reducing tillage reduces the number of implements and tractors and the size of tractors required. 

This reduces emissions from machinery manufacture, transportation, and repair.

18.6.3 N Fertilizer Use

Reducing tillage increases the potential for N losses from the soil and for N tie-up in soil organic 

matter, but this potential is not necessarily realized if N is managed well. If greater N losses 

and/or more N tie-up do occur, more N fertilizer is required to produce a crop. Production of N 

fertilizer requires large quantities of fossil fuel to provide hydrogen and process energy for 

production of anhydrous ammonia and downstream N fertilizers such as urea and ureaammonium nitrate solutions. Additional energy is required to store, distribute, and apply N 

fertilizer. Therefore, any increase in N fertilizer use translates to significant increases in CO2

emissions. The amount of N fertilizer recommended for conservation-till systems is generally 

the same as for intensive-till systems in the Corn Belt. However, N rate recommendations are 

higher for conservation-till systems in some other regions. More details will be provided in the 

case studies.

18.6.4 Herbicide Use

Herbicide manufacture and use require some fossil energy but not nearly as much as required for 

N fertilizer manufacture and use. Reducing tillage results in somewhat more herbicide use but 

the increase in herbicide use is becoming minimal. As herbicide prices decrease and herbicide 

tolerate crops become more widespread, intensive-till and conservation-till systems rely on 

herbicide use to about the same extent. The main difference is that virtually all no-till systems 

use a burn-down contact herbicide before planting whereas intensive-till systems don’t use a 

burndown herbicide before planting.

18.6.5 N2O Emissions From Soil

Increases in N2O emissions can have a large impact on the overall amount of GHG avoided by 

reducing tillage. Small differences in N2O emissions can be important because N2O is 296 times 

more potent (weight basis) than CO2 as a GHG. Reducing tillage increases the potential for 

increased N2O emissions from the soil, but this potential is not necessarily realized if these 

systems are well managed. Emission of N2O from soils results from both denitrification and 

nitrification (more details about these processes in the next paragraph). In general, the amount of 

N2O emitted increases with the amount of N fertilizer applied; this must be considered when 

reducing tillage increases the amount of N fertilizer required. However, even when more N 

fertilizer is not applied with reduced tillage, more N2O may be emitted when tillage is reduced. 

This is because, in some cases, soil conditions with reduced tillage are more conducive to N2O 

emissions from denitrification and nitrification. Therefore, both the amount of N fertilizer 

applied and soil factors must be considered in estimating the effects of reducing tillage on N2O 

emissions.
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-14

Denitrification is a microbial process in which nitrate nitrogen is converted to gases, mainly N2

and N2O, under limited oxygen conditions13. Nitrous oxide is usually a minor portion of the N 

lost via denitrification. In addition to a supply of nitrate and low oxygen conditions, a source of 

biologically available carbon is required as an energy source for the denitrifying microbes. Low 

oxygen conditions occur in soil mainly when soils are excessively wet and soil pores contain 

sufficient water to restrict movement of air into the soil; this can result in a depletion of soil 

oxygen levels as oxygen is used to support plant and microbial growth. In some cases, 

excessively wet conditions and more biologically ava ilable carbon occur more extensively in 

conservation-till than intensive-till systems, leading to more N2O emissions with conservationtill14. More specifics will be provided in the case studies.

Nitrification is the microbial conversion of ammonium to nitrate. Most N fertilizers are applied 

in an ammonium form and N from soil organic matter is released as ammonium. In cropland, 

most of the N supplied from fertilizers and from soil organic matter is ultimately present as 

ammonium and then converted to nitrate via nitrification. Nitrification is a less important source 

of N2O than is denitrification and effects of reducing tillage on N2O emissions from nitrification 

are less well understood than the effects of reducing tillage on N2O emissions from 

denitrification13.

18.7 Costs of Reducing Tillage as a Means of Offsetting Utility GHG 

Emissions

The costs of reducing tillage as a means of offsetting energy company GHG emissions are of 

three types: (1) adoption incentives an energy company would have to pay to get farmers to 

switch from intensive-tillage to conservation-tillage practices on additional land, (2) transaction 

costs for aggregating and brokering GHG credits, and (3) monitoring costs to assure that 

contractual obligations are fulfilled.

In general, crop production costs are less for conservation-tillage systems than for intensivetillage systems and average yields are not significantly affected by tillage system (See Appendix 

E for more details). This means that, in general, farmers’ long-term incomes are not greatly 

affected by switching from intensive till to conservation till. The big economic difference when 

switching to conservation-tillage is greater yield variability and greater risk of reduced income in 

a given year15. Therefore, the ma in cost of achieving increased adoption of conservation tillage 

is an adoption incentive to compensate the farmer for accepting increased risk, especially in the 

first few years after adoption of reduced tillage. The increased risk with conservation-tillage 

systems is usually greatest during the first few years after switching to conservation tillage16. 

This is partly because farmers have to learn how to adapt conservation-tillage systems to their 

specific soils, climate, and financial resources and partly because benefits of improved soil 

quality from conservation tillage may not be significant until several years after switching to 

conservation tillage. Once significant soil quality improvements have occurred, they usually 

translate into higher crop yields. In most cases, the higher crop yields will provide sufficient 

economic incentive to continue the reduced-tillage practices without an external incentive. 

However, in some cases, farmers may have to be compensated for a lower average income over 
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-15

the long term; in these cases, adoption incentives will compensate for lost average income in 

addition to increased risk.

In addition to adoption incentives, other costs of CO2 sink enhancement via reducing tillage are 

transaction costs for aggregating and brokering CO2 credits and monitoring costs to verify that 

contractual obligations are fulfilled. These costs are not well defined because of lack of actual 

implementation projects. Costs will be based on related experiences such as in forestry sink 

enhancement projects.

A key question is how long adoption incentives will have to be paid to a farmer to get him to 

maintain the switch to conservation tillage. There are not definitive answers to this question. As 

indicated in Figures 18-2, 18-5, and 18-6, after switching from intensive-till to conservation-till, 

the annual increases in SOC decrease with time and eventually become zero, usually within 20 to 

40 years after switching to conservation tillage. Will adoption incentives be required for the 

entire 20 to 40 years that SOC is increasing, or even longer? Since the greatest financial risk to 

the farmer occurs in the early years after switching to conservation tillage, it is likely that 

incentive payments will only be necessary for the first 5 to 20 years after switching tillage. After 

that time, higher crop yields with conservation tillage are expected to provide adequate incentive 

to continue conservation-tillage practices. Our analyses in the case studies will assume a range 

of 5 to 20 years for adoption incentive payments.

Another key question is how to handle situations in which a farmer must intensively till 

occasionally (e.g., in one or two years out of 20) to overcome some production problem that 

develops such as the need to incorporate lime or severe rutting of fields because of harvesting 

when the soils were too wet. This project uses a 100-year planning horizon for the economic 

analyses. With this length of planning horizon, we assume that if intensive tillage is used 

occasionally, the SOC level will have time to rebound to the steady-state level under 

conservation tillage before the end of the 100-year planning horizon.

Another key question is how to estimate costs of CO2 abatement from an energy company 

perspective if farmers were to permanently switch back to intensive tillage after adoption 

incentives are no longer paid (as discussed above, this is not expected to be the usual case). As 

discussed in Section 2.4, we use an approach that accounts for the timing of both CO2 abatement 

and CO2 abatement costs. This approach was selected to enable comparison of CO2 storage and 

sink enhancement options that vary greatly in the timing and permanence of CO2 abatement and 

timing of CO2 abatement costs. We treat removals and emissions/leaks of CO2 as separate 

events. The basic idea is that when a company removes or pays for removal of a ton of CO2, the 

company receives the going price of CO2. When a ton of CO2 is released, the owner of this CO2

(in this case the company) must then purchase a credit from elsewhere at the going price. With 

this approach, a breakeven CO2 price ($/tonne of CO2) is calculated as the price at which the sum 

of discounted CO2 revenues (both positive and negative) equals the sum of discounted costs of 

CO2 sink enhancement. This breakeven price is the long-term cost of CO2 abatement. See 

Section 2.4 for calculating costs of storing captured CO2 for more details on calculating the 

breakeven CO2 price.
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-16

A 6.09 percent after-tax discount rate was used in calculating breakeven CO2 prices for CO2 sink 

enhancement via reducing tillage on U.S. croplands. This is the same discount rate (constant 

dollar basis) as used for calculating costs of storing captured CO2, and is applicable for 

calculating CO2 abatement costs from an energy company perspective. An energy company 

likely would use the same financial parameters for evaluating GHG abatement investments 

regardless of whether the investments involve CO2 capture and storage or CO2 sink 

enhancement.

18.8 Cropland Case Studies Included in This Project

The following cropland case studies are included in this project, the goal being to both (1) 

include cases representing the greatest amount of avoided GHG emissions that could be achieved 

by reducing tillage and (2) illustrate the range of amounts per hectare and costs of avoided GHG 

emissions that could be achieved by reducing tillage.

1. Chapter19: Corn/soybean rotations in the Corn Belt being converted from intensive-till to 

either no-till or moderate-till. This case includes best-case and worst-case scenarios 

concerning effects of reducing tillage on N fertilizer requirements and on N2O emissions.

2. Chapter 20: Continuous corn cropping systems in the Corn Belt being converted from 

intensive-till to either no-till or moderate-till. This case includes best-case and worst-case 

scenarios concerning effects of reducing tillage on N fertilizer requirements and on N2O 

emissions.

3. Chapter 21: Wheat/fallow rotations in the western Great Plains being converted from 

intensive-till to no-till. This case includes variations in the amount of SOC sequestered and 

amount of N2O emissions due to reducing tillage.

4. Chapter 22: Intensive-till cotton to no-till cotton in the southeastern U.S. This case includes 

variations in the amount of SOC sequestered and amount of N2O emissions due to reducing 

tillage. 
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-17

18.9 References

1 Lal, R., J.M. Kimble, R.F. Follett, and C.V. Cole. 1999. U.S. cropland’s overall potential to 

mitigate the greenhouse effect. p. 81-87. In R. Lal., J.M. Kimble, R.F. Follett, and C.V. Cole 

(eds.) The potential of U.S. cropland to sequester carbon and mitigate the greenhouse effect. 

Lewis Publishers. Washington D.C.

2 CTIC (Conservation Tillage Information Center). 1999. 

www.ctic.purdue.edu/Core4/Core4TechnicalManual.pdf

3 CTICa (Conservation Tillage Information Center a). 2002. 

www.ctic.purdue.edu/Core4/CT/Definitions.html

4 Brenner, J., K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, T. Kautza, K. Killian, J. 

Schuler and S. Williams. 2001. Quantifying the change in greenhouse gas emissions due to 

natural resource conservation practice application in Iowa. Final report to the Iowa 

Conservation Partnership. Colorado State University Natural Resource Ecology Laboratory 

and USDA Natural Resources Conservation Service, Fort Collins, CO, USA.

5 Smith, P., J. Brenner, K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, K. Killian, D. 

Lamm, J. Schuler and S. Williams. 2002. Quantifying the change in greenhouse gas 

emissions due to natural resource conservation practice application in Indiana. Final report 

to the Indiana Conservation Partnership. Colorado State University Natural Resource 

Ecology Laboratory and USDA Natural Resources Conservation Service, Fort Collins, CO, 

USA.

6 Brenner, J.K., K. Paustian, G. Bluhm, J. Cipra, M.Easter, R. Foulk, K. Killian, R. Moore, J. 

Schuler, P. Smith, and S. Williams. 2002. Quantifying the change in greenhouse gas 

emissions due to natural resource conservation practice application in Nebraska. Colorado 

State University Natural Resource Ecology Laboratory and USDA Natural Resources 

Conservation Service, Fort Collins, CO, USA.

7 Paustian, Keith, Harold P. Collins, and Eldor A. Paul. 1997. Management controls on soil 

carbon. p. 15-49. In E.A. Paul, K. Paustian, E.T. Elliott, and C.V. Cole (eds.) Soil organic 

matter in temperate agroecosystems: long-term experiments in North America.

8 Donigan, A.S. Jr., T.O. Barnwell, R.B. Jackson, A.S. Patwardhan, K.B. Weinreich, A.L. 

Roswell, R.V. Chinnaswamy, and C.V. Cole. 1994. Assessment of alternative management 

practices and policies affecting soil carbon in agroecosystems of the central United States. 

Publication No. EPA/600R-94/067. US-EPA, Athens, GA. 

9 CTICb (Conservation Tillage Information Center b). 2002. 

http://www.ctic.purdue.edu/Core4/CT/transect/50KCroplandAcres.jpg
Economics of CO2 Sink Enhancement Via Reducing Tillage on U.S. Cropland—Basis and Approach

18-18

10 Del Grosso, S.J. W.J. Parton, A.R. Mosier, M.D. Hartman, J. Brenner, D.S. Ojima, and D.S. 

Schimel. 2001. Simulated interaction of carbon dynamics and nitrogen trace gas fluxes using 

the DAYCENT model. P. 303-332. In M.J. Shaffer, Liwang Ma, and S. Hansen (eds.)

Modeling carbon and nitrogen dynamics for soil management. Lewis Publishers. 

Washington, D.C.

11 Peterson, G.A., D.G Westfall, N.E. Toman, and R.L. Anderson. 1993. Sustainable dryland 

cropping systems: economic analysis. Technical BulletinTB93-3. Colorado State University.

12 CTIC (Conservation Tillage Information Center). 2000. 

http://www.ctic.purdue.edu/Core4/CT/ctsurvey/2000/NewsReleases.html

13 Hutchinson, G.I. and Davidson, Eric A. 1993. Processes for production and consumption of 

gaseous nitrogen oxides in soil. p. 79-93. In Dennis E. Rolston (ed.) Agricultural ecosystem 

effects on trace gases and global climate change. ASA Special Publication no. 55. 

ASA,CSSA, and SSSA, Madison, WI.

14 Mummey, Daniel L., Jeffrey L. Smith, and George Blum. 1998. Assessment of alternative 

soil management practices on N2O emissions from U.S. agriculture. Agriculture, 

Ecosystems, and Environment 70:79-87.

15 Kurkalova, Lyubov A., Catherine L. King, and Jinhua Zhao. 2001. The subsidy for adopting 

conservation tillage: estimation from observed behavior. 

http://www.card.iastate.edu/publications/texts/01wp286.pdf

16 CTICc (Conservation Tillage Information Center c). 2002. 

http://www.ctic.purdue.edu/Core4/MAX/1998/Page38.html
19-1

19

CORN/SOYBEAN ROTATIONS IN THE U.S. CORN 

BELT

19.1 Introduction

As indicated in Chapter 18, corn/soybean rotations in the Corn Belt represent the largest 

potential for increasing SOC via reducing tillage in the United States. This is primarily because 

of the large land area in corn/soybean rotations. Also, corn and soybean yields, crop residue 

levels, and potential for per hectare increases in SOC are larger than for regions beyond the Corn 

Belt that use corn/soybean rotations. Base-case results for corn/soybean rotations are presented 

in Section 19.2. The base cases assume good N fertilizer management. Effects of poor N 

fertilizer management on amounts and costs of GHG credits from corn/soybean rotations are 

presented in Section 19.3.

19.2 Corn/Soybean Rotation (Base Cases)

A summary of effects of switching from intensive-till to no- or moderate-till on SOC, GHG 

emissions, and costs of avoided GHG emissions is presented in Table 19-1 for corn/soybean 

rotations in Indiana and Iowa. The bases for the results in Table 19-1 are discussed in the 

following sections using output from the corn/soybean spreadsheet model developed for this 

project.

The results in Table 19-1 are presented at a state level because, even with the relatively large 

land area of corn/soybean rotations in the Corn Belt states, additional adoption of conservationtillage will be required on a state scale to avoid the same quantity of GHG emissions as avoided 

in the CO2 capture and storage cases assessed in this project. For example, converting the basecase 425 MW (net) integrated gasification combined cycle (IGCC) power plant to capture 90 

percent of the CO2 produced and storing the captured CO2 via enhanced oil recovery (EOR) 

avoids 0.45 million tonnes CE of GHG per year on a life-cycle basis during the 20-year life time 

of the plant (see earlier section on EOR). This translates to 9.1 million tonnes CE of GHG 

avoided during the 20 year life of the plant. Converting from intensive-till to no-till in Iowa 

avoids 9.9 tonnes CE GHG emissions per hectare during the 100-year planning horizon used in 

this project (Table 19-1). This means that 9,100,000/9.9=919,000 hectares of intensive-till 

cropland converted to no-till will be required to equal the avoided GHG in the IGCC-EOR case. 

This amount of cropland (0.92 million hectares) is slightly less than the amount of intensively 

tilled soybeans in Iowa and about one-third of the intensively tilled corn in Iowa (Table 18-2).
Corn/Soybean Rotations in the U.S. Corn Belt

19-2

Table 19-1

Effects of switching from intensive-till to no- or moderate-till on SOC, GHG Emissions, and 

costs of life-cycle (LC) avoided GHG emissions--Indiana and Iowa state averages for 

Corn/Soybean rotation

State Indiana Indiana Iowa Iowa

Switching from intensive-till to: No-till Moderate-till

Notill Moderate-till

Quadratic SOC response curve

 ΔSOCs, kg C/ha 7221 5159 9794 6520

 E (slope factor) 53.1 37 68.3 45.6

 Years to new steady state (Ys) 27 28 29 29

Average annual increase in SOC (years 1 to 

Ys), kg C/ha/yr 267 184 338 225

GHG emissions from tillage-system inputs and N2O (years 1-100), kg CE/ha/y:

 Δ Fuel use -22.3 -16.9 -22.3 -16.9

 Δ Machinery maint., trans., and repair -4 -3 -4 -3

 Δ N fertilizer use 0 0 0 0

 Δ Herbicide use 3 0.8 3 0.8

 Δ Total GHG emissions from inputs -23.3 -19.1 -23.3 -19.1

 Δ N2O emissions from soil 30.3 30.3 30.3 30.3

 Δ Total GHG emissions 7.0 11.2 7.0 11.2

Average LC GHG avoided (kg CE/ha/y)

 Years 1 to Ys 260 173 331 214

 Years Ys to 100 -7 -11 -7 -11

Total LC GHG avoided (years 1-100), 

tonne CE/ha 6.5 4.0 9.9 5.4

NPV Total LC GHG avoided (years 1-100), 

tonne CE/ha 4.3 2.9 5.7 3.7

Costs, $/ha/y

 Transaction (year 1) 5 5 5 5

 Adoption incentive (years 1-20) 25 12.5 25 12.5

 Monitoring (years 1-100) 1 1 1 1

Total costs (years 1-100), $/ha 605 355 605 355

NPV Total costs (years 1-100), $/ha 306 163 306 163

Cost (NPV basis), $/tonne CE LC GHG 

avoided 72 56 54 44
Corn/Soybean Rotations in the U.S. Corn Belt

19-3

19.2.1 Increases in Soil Organic Carbon

The increases in SOC due to switching from intensive-till to no-till or moderate-till (Table 19-1) 

are based on state- and county-level assessments for Iowa1 and Indiana2. Iowa and Indiana form 

a representative east-west transect across the mid-section of the Corn Belt states. Similar 

assessments are not available for the other Corn Belt states. The Iowa and Indiana assessments 

provide county-level estimates of increases in SOC for 10 and 20 years after switching 

corn/soybean rotations from intensive-till to no-till or moderate-till. These results were provided 

for several combinations of soil texture and internal drainage [non-hydric (i.e., well drained) or 

hydric (i.e., poorly drained)]. State-level estimates of increases in SOC were provided for

10 years after switching from intensive-till to no-till or moderate-till on both non-hydric and 

hydric soils. SOC response curves for counties representing the range and mean of SOC 

responses in Indiana and Iowa are presented in Appendix F. An interpolation procedure 

described in Appendix F was used to develop state-level SOC response curves for switching 

from intensive-till to no-till and moderate-till in Indiana and Iowa. Graphs of the four SOC 

response curves represented in Table 19-1 are presented in Figure 19-1.

IA and IN State Curves--C/SB

0.0

2000.0

4000.0

6000.0

8000.0

10000.0

0 10 20 30 40

Years after switching tillage

Increase in SOC, kg/ha

IA-NT

IN-NT

IA-MT

IN-MT

Figure 19-1

State SOC response curves for switching from intensive-till to no-till (NT) or moderate-till 

(MT) in Iowa and Indiana

After completing county-level carbon storage estimates first in Iowa1 and then in Indiana2, 

results in Indiana were judged to be most representative of the Corn Belt (personal 

communication, John Brenner, NRCS). Eve et al.3 used the IPCC method of estimating firstapproximation regional increases in SOC due to reducing tillage. The IPCC method assumes a 

20-year linear increase in SOC. Using the IPCC method, switching from intensive tillage to no 

tillage in the Corn Belt resulted in an estimated weighted mean increase in SOC of 480 kg 

C/ha/yr for a 20-year period. This estimated increase in SOC should be representative for a 

corn/soybean rotation since it is the predominant cropping system in the Corn Belt. A best-fit 

linear/plateau curve for the Indiana state curve (intensive till to no till) in Figure 19-1 gave a 

linear SOC increase of 400 kg C/ha/yr until the plateau was reached after 18 years. This is very 

similar to the IPCC estimate of 480 kg/ha/yr for a 20-year period, further supporting use of the 

Indiana state curves as being more representative of the Corn Belt than are the Iowa state curves. 
Corn/Soybean Rotations in the U.S. Corn Belt

19-4

West and Post4 summarized increases in SOC in the United States due to switching corn/soybean 

rotations from intensive tillage to no tillage for a period of 10 to 15 years; they estimated a mean 

increase in SOC of 900 kg C/ha/yr. This is much higher than the estimates by either Eve et al.3

using IPCC methodology or Brenner et al.1 on which the curves in Figure 19-1 are based. The 

higher values from West and Post4 appear to be due at least partially to the fact that most of the 

studies they summarized are for a shorter time period (10-15 years) after switching to no tillage, 

thereby reflecting a steeper portion of the SOC response curve.

Example spreadsheet inputs for calculating increases in SOC due to switching from intensive-till 

to no-till in Indiana (first column of Table 19-1) are presented in Table 19-2.

Table 19-2

Example process and economic input variables for switching from intensive-till to no-till 

for corn/soybean rotation in Indiana

Process

Alternative Tillage System:

Calculation Method for Sequestering Soil Organic Carbon (SOC)

Quadratic Plateau

Linear Plateau

Efficiency or Slope Index (E) 53.1

SOC Increase to New Steady State (ΔSOCs) 7221.0 kg/ha

Years until New Steady State (Ys) 27 years

No-till time period 100 years

SOC Release Rate after no-till time period 1500 kg C/ha/yr

Economic

Cost

$/ha/yr

Transaction Costs ( years 1- 1 ) 5.00

Farmer Adoption Incentive to Reduce Tillage ( years 1- 20 ) 25.00

Monitoring Costs ( years 1- 100 ) 1.00

Discount rate 6.09%

Cropland: Two-Year Corn/Soybean Rotation

Process and Economic Input Variables

(Changes Due to Switching from Intensive-Till to No-Till)

Indiana

No-till

 
Corn/Soybean Rotations in the U.S. Corn Belt

19-5

19.2.2 Costs

Example cost inputs are presented in Table 19-2. Experience with conservation-tillage adoption 

programs indicates that an adoption incentive of at least $25/ha/year for corn/soybean rotations 

will be required to achieve additional adoption from intensive-till of no-till in the Corn Belt 

(personal communication, Dan Towery, Conservation Tillage Information Center). For example, 

the Environmental Quality Incentives Program (EQIP) in Illinois is providing an incentive of 

$50/ha/yr for switching corn from intensive tillage to no tillage and is providing no incentive for 

switching soybeans from intensive tillage to no tillage. This results in an average incentive of 

$25/ha/yr for corn/soybean rotations. Conservation-tillage adoption incentives are estimated in 

Appendix E and ranged from $20 to 25/ha/year for switching from intensive-till to no-till 

corn/soybean rotations. A value of $25/ha/year was used for the case studies in this chapter. As 

a first approximation, we assume an adoption incentive of $12.50/ha/yr will be required to 

achieve additional adoption of corn/soybean rotations from intensive-till to moderate till. 

Transaction costs are based on experience with forestry projects and are estimated at $5/ha. 

Monitoring costs are assumed to be less than for forestry ($5/ha), assuming that cropland 

monitoring will be based mainly on monitoring practices that have been correlated with changes 

in SOC rather than direct measurements of SOC in each field. Monitoring costs of $1/ha/year 

were assumed.

19.2.3 GHG Effects in Addition to Carbon Storage in Soil Organic Matter

19.2.3.1 Fuel Use

Reductions in fuel use and associated carbon emissions by switching from intensive-till to no-till 

in a corn/soybean rotation are presented in Table 19-3. Fuel usage rates are from Langemeier 

and Taylor5. Reductions in fuel use and associated carbon emissions by switching from 

intensive-till to moderate-till in a corn/soybean rotation are presented in Table 19-4.
Corn/Soybean Rotations in the U.S. Corn Belt

19-6

Table 19-3

Reductions in fuel use and associated carbon emissions by switching from intensive-till to 

no-till in a corn/soybean rotation

gal./acre L/ha gal./acre L/ha kg C/ha

Intensive-till: Intensive-till:

Moldboard plow 1.68 15.71 Disk corn stalks 0.45 4.21 3.6

Disk 0.55 5.14 Moldboard plow 1.68 15.71 13.3

Field cultivate 0.60 5.61 Disk 0.55 5.14 4.4

Plant 0.50 4.68 Field cultivate 0.60 5.61 4.8

Cultivate 0.45 4.21 Plant 0.50 4.68 4.0

Total 3.78 35.34 Total 3.78 35.34 30.0

No-till: No-till:

Burndown herbicide application 0.10 0.94 Burndown herbicide application 0.10 0.94 0.8

Plant 0.50 4.68 Shred Corn Stalks 0.75 7.01 6.0

0.00 Plant 0.50 4.68 4.0

Total 0.60 5.61 Total 1.35 12.62 10.7

Tillage-system difference Tillage-system difference -19.3

Mean annual tillage-system difference ( years 1- 100 ) -22.3 kg C/ha

Annual fuel saving from reducing tillage -22.3 kg C/ha

Annual machinery MTR savings 18% of fuel savings

Annual machinery MTR saving from reducing tillage ( years 1- 100 ) -4.0 kg C/ha

Cropland: Two-Year Corn/Soybean Rotation

Fuel Use for tillage, planting, and herbicide operations

4.8

Annual Fuel Use

Corn Portion Soybean Portion

kg C/ha

Transportation, & Repair (MTR)

Energy Savings Embodied in Machinery Manufacturing, 

4.8

4.0

3.6

30.0

0.8

4.0

-25.2

13.3

4.4

Annual Fuel Use

Indiana

19.2.3.2 Machinery Manufacture, Transportation, and Repair

Reductions in machinery manufacture, transportation, and repair (MTR) and associated carbon 

emissions by switching from intensive-till to no-till or moderate-till in a corn/soybean rotation 

were calculated as 18 percent of the fuel savings (Tables 19-3 and 19-4). The 18 percent factor 

is based on ratios of tillage-system fuel use and machinery MTR in a national analysis of tillagesystem effects on GHG emissions6

.
Corn/Soybean Rotations in the U.S. Corn Belt

19-7

Table 19-4

Reductions in fuel use and associated carbon emissions by switching from intensive-till to 

moderate-till in a corn/soybean rotation

gal./acre L/ha gal./acre L/ha kg C/ha

Intensive-till: Intensive-till:

Moldboard plow 1.68 15.71 Disk corn stalks 0.45 4.21 3.6

Disk 0.55 5.14 Moldboard plow 1.68 15.71 13.3

Field cultivate 0.60 5.61 Disk 0.55 5.14 4.4

Plant 0.50 4.68 Field cultivate 0.60 5.61 4.8

Cultivate 0.45 4.21 Plant 0.50 4.68 4.0

Total 3.78 35.34 Total 3.78 35.34 30.0

Moderate-till: Moderate-till:

Disk 0.55 5.14 Disk 0.55 5.14 4.4

Field Cultivate 0.60 5.61 Field Cultivate 0.60 5.61 4.8

Plant 0.50 4.68 Plant 0.50 4.68 4.0

Total 1.65 15.43 Total 1.65 15.43 13.1

Tillage-system difference Tillage-system difference -16.9

Mean annual tillage-system difference ( years 1- 100 ) -16.9 kg C/ha

Annual fuel saving from reducing tillage -16.9 kg C/ha

Annual machinery MTR savings 18% of fuel savings

Annual machinery MTR saving from reducing tillage ( years 1- 100 ) -3.0 kg C/ha

Cropland: Two-Year Corn/Soybean Rotation

Fuel Use for tillage, planting, and herbicide operations

4.0

13.1

Annual Fuel Use

Corn Portion Soybean Portion

kg C/ha

Transportation, & Repair (MTR)

Energy Savings Embodied in Machinery Manufacturing, 

4.8

4.0

3.6

30.0

4.4

4.8

-16.9

13.3

4.4

Annual Fuel Use

Indiana

19.2.3.3 Nitrogen Fertilizer Use

N fertilizers generally are not applied on soybeans because they are legumes. When inadequate 

soil N is available to legumes, they convert N2 from the atmosphere into forms available for 

plant nutrition, a process known as biological N fixation. Therefore, reducing tillage has no 

impact on the amount of N fertilizer used in the soybean portion of a corn/soybean rotation.

In addition to not requiring N fertilizer, soybeans generally leave more readily available N for 

the next crop than does corn. In the Corn Belt, it is common to reduce N fertilizer 

recommendations for corn following soybeans by about 30 kg N/ha relative to recommendations 

for corn following corn7. This credit is not adjusted for tillage system.

In contrast to soybeans, corn generally requires significant quantities of N fertilizer for 

economically optimum production. There are several mechanisms by which more N can be lost 

from the soil when tillage is reduced, especially when N is poorly managed8,9. Reducing tillage 

conserves soil moisture. Higher soil moisture levels increase the potential for N leaching losses 
Corn/Soybean Rotations in the U.S. Corn Belt

19-8

via downward movement with soil water, especially in highly permeable soils. No-till soils tend 

to develop more macro-pores that potentially can cause greater N leaching losses, especially 

when N is applied on the soil surface and N fertilizer moves directly into macro-pore openings at 

the soil surface. Higher soil moisture levels also increase the potential for N losses via 

denitrification, especially with wet conditions in soils with poor internal drainage (see 

Chapter 18 discussion of denitrification in the section about N2O losses via denitrification). 

More readily available organic matter near the soil surface in conservation-tillage systems also 

increases the potential for denitrification, especially when N is applied on the soil surface. The 

potential for denitrification can be greatly reduced by injecting N below the crop residues that 

are concentrated near the soil surface in conservation-till systems. The potential for both 

denitrification and leaching losses can be significantly reduced by applying N close to the time 

of crop need. When urea-containing fertilizers are applied on the soil surface without immediate 

incorporation, the potential for ammonia volatilization losses is much greater in conservation-till 

systems and especially in no-till systems. This problem is virtually eliminated when ureacontaining fertilizers are injected into the soil. In summary, the potential for N losses is greater 

with conservation tillage than with intensive tillage when N is poorly managed. With 

conservation tillage, it is more important that N be applied close to the time of crop need and that 

the N be injected below the decomposing crop residues.

Minimizing contact of N fertilizers with crop residues is especially critical in conservation-tillage 

systems10. In the Corn Belt, most of the N is applied as anhydrous ammonia and by necessity is 

injected well below the soil surface. Injection reduces the potential for denitrification and N tieup in organic matter in conservation-tillage systems. In contrast to urea or urea-ammonium 

nitrate solution, ammonia volatilization from anhydrous ammonia generally is not a significant 

problem and is not likely to differ by tillage system. With timely application of anhydrous 

ammonia, N leaching is not likely to be significantly greater in conservation-tillage systems than 

intensive-tillage systems. Outside the Corn Belt, anhydrous ammonia is not the dominant form 

of N fertilizer and surface applications of N fertilizer are more common. In some of these cases 

N rate recommendations are higher for conservation-till systems, especially no-till. An example 

is in Kentucky in which N rate recommendations are increased about 25 kg/ha for no-till corn on 

soils that are not well drained11.

In addition to N losses, the amount of N released from crop residues and soil organic matter 

affects the amount of N fertilizer required. In general, N mineralization (conversion from 

organic to inorganic N) is slower with conversation tillage than with intensive tillage, especially 

with cool wet conditions in the spring. When soil organic matter leve ls increase, more N as well 

as C is sequestered in the organic matter. Soil C:N ratios usually stabilize at about 11:1. This 

implies that if the SOC level increases 338 kg/ha/yr due to reducing tillage (see Iowa No-till 

example in Table 19-1), then the amount of N tied up in soil organic matter should increase by 

about 30 kg N/ha/yr. In some cases, the added N tied up in soil organic matter with 

conservation tillage may be N that would have been lost had it not been tied up. In that case, the 

amount of N fertilizer required is not affected by N tie-up in organic matter. Also, it should be 

noted that if conservation-tillage corn residues mineralize less N to become available to the 

subsequent soybean crop, the soybean N-fixation mechanism likely makes up for most or all of 

that deficit.
Corn/Soybean Rotations in the U.S. Corn Belt

19-9

Nitrogen rate recommendations generally are adjusted for yield levels expected to be achieved in 

a given cropping system. For individual farms and years, tillage system can significantly affect 

yields. However, averaged over a broad area (e.g., a state) and several years, tillage system is 

expected to have little impact on yields and associated N fertilizer rates.

In summary, the issue of amount of N fertilizer required by differing tillage systems is relevant 

for only the corn portion of a corn/soybean rotation and the soybean N fixation mechanism 

probably largely compensates for any reduced mineralization of soil organic matter following the 

corn crop. The principles discussed above indicate that when N is injected well below the 

decomposing crop residues and is applied close to when the crop needs the N, then the amount of 

N fertilizer required for Corn in the Corn Belt should not be significantly affected by tillage 

system. However, with poorly managed N, more N fertilizer will be required with conservation 

tillage than with intensive tillage.

The N fertilizer recommendations for Michigan, Ohio, and Indiana have been integrated into one 

system7 and are representative of the Corn Belt. The recommended N rate is not adjusted for 

tillage system. However, the following adjustments are made for tillage system:

1. For soils with greater than 30 percent residue cover, the majority of applied N should be 

either:

a. Injected below the soil surface

b. Dribbled if using N solutions

c. Or broadcast only if the material contains no urea

2. No-till corn planted into cold, wet soils should receive some of the recommended N at 

planting banded near the row (20 to 40 kg N/ha)

The National Agricultural Statistical Service reported the following average N rates for corn in 

the 10 major corn states as affected by tillage system and manure application (Table 19-5). 

Significantly less N fertilizer per hectare and more manure were applied in intensive-till systems 

involving plowing than in the other tillage systems reported. The lower mean N fertilizer rate for 

intensive-till systems involving plowing appears to be due to more manure being used in these 

systems, not because this tillage system requires less N fertilizer per se. Less N fertilizer is 

recommended on fields receiving manure, regardless of tillage system12.

Table 19-5

Mean N rates for corn in the ten major U.S. corn producing states--199513

Intensive -till

with plow

Intensive -till

without plow Mulch-till No-till

N rate, kg/ha 108 148 150 150

% of fields with

N fertilizer 93 98 97 98

% of fields with 

manure 38

15

14 8
Corn/Soybean Rotations in the U.S. Corn Belt

19-10

In this corn/soybean rotation case study, it was assumed that N rate is not affected by tillage 

system. This is because N rate recommendations are not adjusted for tillage system in the Corn 

Belt and the mean of actual N rates for corn after correcting for manure application, don’t differ 

by tillage system.

Effects of applying more N fertilizer with no-till than with intensive-till, a case that is applicable 

when N fertilizer is poorly managed, is presented in Section 19-3.

19.2.3.4 Herbicide Use

The mean annual herbicide rates for intensive-till and no-till corn/soybean rotations were 

estimated from West and Marland6 as 1.92 and 2.56 kg active ingredient (a.i.)/ha, respectively. 

West and Marland6 estimated 4.70 kg C emitted/kg herbicide a.i. used. With these factors, 

carbon emissions were 3.0 kg C/ha/yr more for no-till than intensive-till. The mean annual 

herbicide rate for moderate-till corn/soybean rotations was estimated from West and Marland6 as 

2.09 kg a.i./ha. Multiplying (2.09-1.92 kg a.i./ha) by 4.70 kg C emitted/kg herbicide gave 0.8 kg 

C/ha/yr more for moderate-till than no-till. Example spreadsheet output is presented in Table 

19-6.

Table 19-6

Example spreadsheet output for estimating herbicide carbon equivalent emissions for 

corn/soybean rotation

Mean annual

herbicide rate

kg a.i./ha kg C/kg herbicide (a.i.) kg C/ha/yr

Intensive-till corn/soybean rotation 1.92

No-till corn/soybean rotation 2.56

Difference ( years 1- 100 ) 0.64 4.70 3.0

Cropland: Two-Year Corn/Soybean Rotation

Annual Herbicide Use X Tillage System

Indiana

19.2.3.5 N2O Emissions from Soil

Background on N2O emissions from soil was provided earlier, indicating that N2O emissions are 

dependent on both soil conditions and amount of N fertilizer applied. In this Corn Belt 

corn/soybean rotation case, the amount of N fertilizer applied does not change with tillage 

system, but soil conditions do change as discussed earlier. Robertson et al.14 measured effects of 

switching from intensive-till to no-till in a corn/wheat/soybean rotation on a well drained soil in 

Michigan. The same N rate was used for both tillage systems and anhydrous ammonia was the 

primary N source. Switching to no-till increased N2O emissions from 141 to 152 kg CE/ha/yr. 

Del Grosso et al.15 modeled N2O emissions as affected by switching corn/soybean rotations from 

intensive-till to no-till on a well drained soil in Indiana. The same N fertilizer rate was assumed 
Corn/Soybean Rotations in the U.S. Corn Belt

19-11

for both tillage systems. A specific placement (e.g., knifing or broadcast) of the N fertilizer was 

not indicated but broadcast placement is assumed. With these assumptions, N2O emissions were 

slightly lower with no-till than intensive-till in both a corn/soybean rotation and continuous corn. 

Mummey et al.16 modeled N2O emissions for intensive-till and no-till systems at 1035 sites for 

corn and 655 sites for soybeans. These sites represented the range of soil and environmental 

conditions for these crops in the United States. This modeling effort indicated that in the north 

central U.S. the mean N2O-N emissions were 3.04 kg/ha for intensive-till and 3.28 kg/ha for notill. Taking the difference (0.24 kg N2O-N/ha) times 126.4 kg N2O CE/kg N2O-N gives 30.3 kg 

N2O CE/ha/yr. This is a larger increase in N2O emissions due to switching to no-till than was 

found by Robertson et al.14 and Del Grosso et al.15, but the latter two studies were on well 

drained soils whereas the Mummey et al.16 study covered a range of soil and environmental 

conditions. On the other hand, Mummey et al.16 did not account for possible reductions in 

denitrification and N2O emissions with knifed N fertilizer applications that are common with 

anhydrous ammonia in the Corn Belt. The Mummey et al.16 estimates of increased N2O 

emissions due to switching from intensive-till to no-till in the north central U.S. were used as 

inputs to generate results in Table 19.1 for the corn/soybean Corn Belt case study (base 

scenario). Example spreadsheet output is presented in Table 19-7.

Table 19-7

Example spreadsheet output for carbon equivalent emissions from changes in N2O 

emissions for corn/soybean rotation when changing from intensive-till to no-till

Annual

kg N2O-N/ha kg N2O CE/kg N2O-N kg N2O CE/ha/yr

Intensive-till corn/soybean rotation 3.04

No-till corn/soybean rotation 3.28

Difference ( years 1- 100 ) 0.24 126.4 30.3

Mean annual

N rate IPCC emission factor

kg N/ha kg N2O CE /kg N applied kg N2O CE/ha/yr

Intensive-till corn/soybean rotation 75.0

No-till corn/soybean rotation 75.0

Difference ( years 1- 100 ) 0.0 2.65 0.0

Total Difference ( years 1- 100 ) 30.3

Associated with Changes in N Application Rate

Due to Tillage-Related Changes in Soil Properties & Micro-Climate

Cropland: Two-Year Corn/Soybean Rotation

Annual Changes in N2O Emissions

Indiana
Corn/Soybean Rotations in the U.S. Corn Belt

19-12

19.2.3.6 Summary of Avoided GHG Emissions

Example spreadsheet output summarizing changes in GHG emissions is presented in Table 19-8. 

This output corresponds to the first column in Table 19-1.

Table 19-8

Example spreadsheet output summarizing changes in GHG emissions for corn/soybean 

rotation when changing from intensive-till to no-till

Changes in GHG Emissions

kg CE/ha/yr

Average Soil Organic Carbon (SOC) sequestered ( years 1 - 27 ) -267

CO2 Emissions from Tillage-System Inputs: ( years 1 - 100 )

Fuel -22.3

Machinery MTR -4.0

N fertilizer 0.0

Herbicides 3.0

Total -23.3

N2O Emissions from Soil ( years 1 - 100 ) 30.3

Avoided GHG Emissions

kg CE/ha/yr tonne CE/ha/yr

( years 1 - 27 ) 260.4 0.260

( years 28 - 100 ) -7.1 -0.007

Annual Average

Cropland: Two-Year Corn/Soybean Rotation

Life Cycle (LC) Avoided GHG Emissions

(Due to Switching from Intensive-Till to No-Till)

Indiana

19.2.4 Summary

Example spreadsheet output summarizing costs and life-cycle (LC) avoided GHG on a carbon 

equivalent (CE) basis is presented in Table 19-9. This output corresponds to the first column in 

Table 19-1.
Corn/Soybean Rotations in the U.S. Corn Belt

19-13

Table 19-9

Example spreadsheet output summarizing costs and life-cycle (LC) avoided GHG on a 

carbon equivalent (CE) basis for corn/soybean rotation when changing from intensive-till 

to no-till

Process Description

Calculation Method for Sequestering SOC - Quadratic Plateau No-till time period 100 years

Average SOC Sequestered 267 kg C/ha/yr Release Rate after No-till 1.5 tonne CE/ha/yr

Time until Steady State 27 years

Economic

Cost

($/ha/yr) ($/tonne LC avoided CE)

Average Annual Costs ( years 1 - 1 ) 31.00 56.93

( years 2 - 20 ) 26.00 74.44

( years 21 - 27 ) 1.00 10.48

( years 28 - 100 ) 1.00 -141.45

($/ha) ($/tonne LC avoided CE)

CO2 Sink Enhancement, NPV Basis ( years 1 - 100 ) 305.75 71.68

Cropland: Two-Year Corn/Soybean Rotation

 Summary

Indiana

LC Avoided CE

(tonne/ha)

4.27

(tonne/ha/yr)

0.545

0.349

0.095

-0.007

-2

-1

0

1

2

3

4

5

6

7

8

0 20 40 60 80 100

Years

Avoided Emissions (tonneCE/ha) 

SOC Non-SOC

Total Avoided GHG Discounted Total Avoided GHG

0

100

200

300

400

500

600

700

0 20 40 60 80 100

Years

Cost ($ha) 

Undiscounted Cost Discounted Cost
Corn/Soybean Rotations in the U.S. Corn Belt

19-14

19.2.5 Sensitivity Summary

Example spreadsheet sensitivity summary output is presented in Table 19-10. This sensitivity 

output presents costs assuming adoption incentive payments for 5, 10, 15, and 20 years. These 

costs are presented assuming that no-till is maintained for the full 100-year planning horizon or 

for only as long as adoption incentives are paid to the farmer, after which the land is converted 

back to intensive-till. This analysis assumes that when no-till is switched back to intensive-till, 

SOC levels decrease at a rate of 1500 kg/ha/yr. A base case of incentive payments for 10 years 

was selected for comparing costs with other CO2 sink enhancement or storage options.
Corn/Soybean Rotations in the U.S. Corn Belt

19-15

Table 19-10

Corn/soybean rotation sensitivity summary

Current Input Variable Values

Calculation Method for Sequestering CO2 Quadratic Plateau

Efficiency Index (Slope Factor) 53.1

SOC Increase to New Steady State (kg/ha) 7221

Time Until Steady State (years) 27

SOC Release Rate after no-till time period (tonne C/ha/yr) 1.5

Farmer Adoption Incentive to Reduce Tillage ($/ha/yr) $25.00

Transaction Costs $5.00

Transaction Time Period (years) 1

Monitoring Costs $1.00

Monitoring Time Period (years) 100

After-Tax Discount Rate 6.09%

Sensitivity Input

Farmer Adoption Incentive Time Period Viewed 5 years

10 years

15 years

20 years

Push the adjacent button to update the sensitivity summary costs below.

CO 2 Sink Enhancement Costs (100 year summation)

No-till Time Farmer Adoption Incentive NPV Basis

Period Time Period LC avoided basis

(years) (years) ($/tonne C equivalent)

5 5 358.50

10 10 193.75

15 15 146.75

20 20 126.53

100 5 29.57

100 10 47.90

100 15 61.53

100 20 71.68

Cropland: Two-Year Corn/Soybean Rotation

Indiana

Sensitivity Summary

Update

Values
Corn/Soybean Rotations in the U.S. Corn Belt

19-16

19.3 Corn/Soybean Rotations (Poor N Management Scenario)

This case is the same as the Indiana no-till base case in the last section except that poorer N 

management (broadcast application of urea) is assumed for the corn portion of the rotation, 

resulting in more N fertilizer being required with no-till corn than intensive-till corn. Using 

more N fertilizer increases GHG emissions associated with N fertilizer use and also increases 

N2O emissions in alternating years when corn is grown. This case assumes 150 kg N/ha for 

intensive-till and 175 kg N/ha or 200 kg/ha for no-till. These N rates fall within the 

recommended rates for intensive-till and no-till corn in Kentucky11.

Table 19-11

Effects of switching from intensive-till using 150 kg N/ha to no- till using 175 or 200 kg 

N/ha on GHG emissions and costs of life-cycle (LC) avoided GHG emissions—Indiana 

Corn/Soybean rotations

Switching from intensive-till to: No-till No-till No-till

N rate, kg/ha 150 175 200

Quadratic SOC response curve

 ΔSOCs, kg C/ha 7221 7221 7221

 E (slope factor) 53.1 53.1 53.1

 Years to new steady state (Ys) 27 27 27

 Average annual increase in SOC (years 1 to Ys), 

 kg C/ha/yr 267 267 267

GHG emissions from tillage-system inputs and N2O (years 1-100), kg CE/ha/y:

 Δ Fuel use -22.3 -22.3 -22.3

 Δ Machinery maintenance, trans., and repair -4 -4 -4

 Δ N fertilizer use 0 14.4 28.7

 Δ Herbicide use 3 3 3

 Δ Total GHG emissions from inputs -23.3 -8.9 5.4

 Δ N2O emissions from soil 30.3 63.5 96.6

 Δ Total GHG emissions 7.0 54.5 102

Average LC GHG avoided (kg CE/ha/y)

 Years 1 to Ys 260 213 165

 Years Ys to 100 -7 -55 -102

Total LC GHG avoided (years 1-100), tonne CE/ha 6.5 1.8 -3.0

NPV Total LC GHG avoided (years 1-100), tonne 

CE/ha 4.3 3.5 2.7

Costs, $/ha/y

 Transaction (year 1) 5 5 5

 Adoption incentive (years 1-20) 25 25 25

 Monitoring (years 1-100) 1 1 1

Total costs (years 1-100), $/ha 605 605 605

NPV Total costs (years 1-100), $/ha 306 306 306

Cost (NPV basis), $/tonne CE LC GHG avoided 72 88 113
Corn/Soybean Rotations in the U.S. Corn Belt

19-17

A summary of results is presented in Table 19-11. Increasing the N rate every other year for 

corn by 25 kg N/ha increased the cost of avoided GHG from $72 to $88/tonne CE LC avoided. 

Increasing the N rate for corn by 50 kg N/ha increased the cost of avoided GHG from $72/tonne 

CE LC avoided to $113/tonne CE LC avoided on an NPV basis. These increased costs are due 

to less avoidance of GHG emissions per dollar invested in adoption incentives and transaction 

and monitoring costs. Adding an additional 50 kg N/ha every other year for corn nearly doubled 

the cost per tonne CE LC avoided on an NVP basis and resulted in a negative avoidance of GHG 

emissions on a nondiscounted basis after 100 years (a decrease from 6.5 to -3.0 tonnes CE LC 

avoided). This emphasizes the importance of efficient N fertilizer management in cropping 

systems designed to reduce GHG emissions. Details of the effects of increased N fertilizer use 

on GHG emissions from N fertilizer manufacture, transportation, storage, and application and on 

N2O emissions are presented in the next two sections.

19.3.1 Nitrogen Fertilizer Use

Carbon emissions associated with important combinations of N source and placement are 

presented in Table 19-12. Emissions from N fertilizer use include manufacture17 and 

transportation and storage18. Emissions from N fertilizer application are from Langemeier and 

Taylor5. The manufacturing numbers assume that all energy is from natural gas, but a small 

undermined amount of energy is from electricity. The transportation, storage, and application 

numbers assume the energy is from diesel fuel.

Table 19-12

Corn/soybean carbon emissions associated with important combinations of N source and 

placement

N Appln. Total N Appln. Total

Source/Placement kg N/ha kg C/kg N kg C/ha kg C/ha Source/Placement kg N/ha kg C/kg N kg C/ha kg C/ha kg C/ha

Intensive-till: Intensive-till:

Anhydrous ammonia/knifed 0.0 0.838 0 0.0 Anhydrous ammonia/knifed 0.0 0.838 0.0 0.0 0.0

Urea/broadcast 150.0 1.148 1.6 173.8 Urea/broadcast 0.0 1.148 0.0 0.0 0.0

UAN solution/broadcast 0.0 1.012 0.0 0.0 UAN solution/broadcast 0.0 1.012 0.0 0.0 0.0

UAN solution/knifed 0.0 1.012 0.0 0.0 UAN solution/knifed 0.0 1.012 0.0 0.0 0.0

Ammonium nitrate/broadcast 0.0 1.021 0.0 0.0 Ammonium nitrate/broadcast 0.0 1.021 0.0 0.0 0.0

Total 150.0 1.6 173.8 Total 0.0 0.0 0.0 0.0

No-till: No-till:

Anhydrous ammonia/knifed 0.0 0.838 0 0.0 Anhydrous ammonia/knifed 0.0 0.838 0.0 0.0 0.0

Urea/broadcast 175.0 1.148 1.6 202.5 Urea/broadcast 0.0 1.148 0.0 0.0 0.0

UAN solution/broadcast 0.0 1.012 0.0 0.0 UAN solution/broadcast 0.0 1.012 0.0 0.0 0.0

UAN solution/knifed 0.0 1.012 0.0 0.0 UAN solution/knifed 0.0 1.012 0.0 0.0 0.0

Ammonium nitrate/broadcast 0.0 1.021 0.0 0.0 Ammonium nitrate/broadcast 0.0 1.021 0.0 0.0 0.0

Total 175.0 1.6 202.5 Total 0.0 0.0 0.0 0.0

Tillage-system difference 25.0 28.7 Tillage-system difference 0.0 0.0 0.0

N Appln. Total

kg C/ha kg C/ha

Mean annual tillage-system difference ( years 1- 100 ) 12.5 14.4 14.4

Cropland: Two-Year Corn/Soybean Rotation

N Fertilizer Use X Crop and Tillage System

Annual N fertilizer use

Corn Portion Soybean Portion

Annual N fertilizer use

kg N/ha

Annual N fertilizer use

kg C/ha

0.0

172.2

0.0

0.0

Indiana

0.0

172.2

28.7

0.0

200.9

0.0

0.0

0.0

200.9

This case assumes that no N fertilizer is applied for soybeans and urea is broadcast for corn in 

both the intensive-till and no-till systems and that 150 kg N/ha and 175 kg N/ha are required for 
Corn/Soybean Rotations in the U.S. Corn Belt

19-18

intensive-till and no-till corn , respectively. This resulted in an average annual increase in 

N fertilizer use of 12.5 kg N/ha.

The increase in N fertilizer use with no-till was assumed to increase N2O emissions from soils in 

accordance with the IPCC default N2O emission factor for commercial N fertilizer use19. The 

IPCC factor assumes the following:

• Direct N2O-N emissions = 1.25 percent of applied N

• Indirect N2O-N emissions from:

Volatilized NH3 and NOx = 0.1 percent of applied N

Leached N = 0.75 percent of applied N

• Total N2O-N emission = 2.1 percent of applied N

(i.e., 0.021 kg N2O-N/kg applied N)

The IPCC factor is converted to a carbon equivalent (CE) radiative forcing basis as follows:

• 1.57 N2O/N2O-N 

• 296 CO2 equivalent/N2O

• 0.272 C equivalent (CE)/CO2 equivalent

• 1.57 x 296 x .272 CE/N2O-N

(126.4 CE/N2O-N)

Therefore, 1 kg applied N = 0.021 x 126.4 kg CE from N2O

 = 2.65 kg CE from N2O

The overall increase in N2O emissions due to both switching from intensive-till to no-till and 

increasing N fertilizer use was calculated as shown in Table 19-13. Increased N2O emissions 

due to tillage-related changes in soil properties and micro-climate (without changes in 

N fertilizer rate) are based on the same assumptions as in the previous case in which N fertilizer 

rate didn’t change with tillage system.
Corn/Soybean Rotations in the U.S. Corn Belt

19-19

Table 19-13

Corn/soybean overall increase in N2O emissions due to both switching from intensive-till 

to no-till and increasing N fertilizer use

Annual

kg N2O-N/ha kg N2O CE/kg N2O-N kg N2O CE/ha/yr

Intensive-till corn/soybean rotation 3.04

No-till corn/soybean rotation 3.28

Difference ( years 1- 100 ) 0.24 126.4 30.3

Mean annual

N rate IPCC emission factor

kg N/ha kg N2O CE /kg N applied kg N2O CE/ha/yr

Intensive-till corn/soybean rotation 87.5

No-till corn/soybean rotation 75.0

Difference ( years 1- 100 ) 12.5 2.65 33.1

Total Difference ( years 1- 100 ) 63.5

Associated with Changes in N Application Rate

Due to Tillage-Related Changes in Soil Properties & Micro-Climate

Cropland: Two-Year Corn/Soybean Rotation

Annual Changes in N2O Emissions

Indiana

19.3.2 Summary of Avoided GHG Emissions

Example spreadsheet output summarizing changes in GHG emissions due to switching from 

intensive-till to no-till and increasing N fertilizer use by 25 kg N/ha is presented in Table 19-14. 

These results correspond to the second column in Table 19-11. Over the course of the 100-year 

planning horizon, the added N fertilizer use and associated increase in N2O emissions 

substantially reduced the amount of GHG emissions avoided (Table 19-15).
Corn/Soybean Rotations in the U.S. Corn Belt

19-20

Table 19-14

Corn/soybean example spreadsheet output summarizing changes in GHG emissions due 

to switching from intensive-till to no-till and increasing N fertilizer use

Changes in GHG Emissions

kg CE/ha/yr

Average Soil Organic Carbon (SOC) sequestered ( years 1 - 27 ) -267

CO2 Emissions from Tillage-System Inputs: ( years 1 - 100 )

Fuel -22.3

Machinery MTR -4.0

N fertilizer 14.4

Herbicides 3.0

Total -8.9

N2O Emissions from Soil ( years 1 - 100 ) 63.5

Avoided GHG Emissions

kg CE/ha/yr tonne CE/ha/yr

( years 1 - 27 ) 212.9 0.213

( years 28 - 100 ) -54.5 -0.055

Annual Average

Cropland: Two-Year Corn/Soybean Rotation

Life Cycle (LC) Avoided GHG Emissions

(Due to Switching from Intensive-Till to No-Till)

Indiana

19.3.3 Summary

Example spreadsheet output summarizing costs and life-cycle (LC) avoided GHG on a carbon 

equivalent (CE) basis is presented in Table 19-15. This output corresponds to the second column 

in Table 19-11.
Corn/Soybean Rotations in the U.S. Corn Belt

19-21

Table 19-15

Corn/soybean example spreadsheet output summarizing costs and life-cycle (LC) avoided 

GHG on a carbon equivalent (CE) basis when changing from intensive-till to no-till and 

increasing N fertilizer use

Process Description

Calculation Method for Sequestering SOC - Quadratic Plateau No-till time period 100 years

Average SOC Sequestered 267 kg C/ha/yr Release Rate after No-till 1.5 tonne CE/ha/yr

Time until Steady State 27 years

Economic

Cost

($/ha/yr) ($/tonne LC avoided CE)

Average Annual Costs ( years 1 - 1 ) 31.00 58.47

( years 2 - 20 ) 26.00 77.63

( years 21 - 27 ) 1.00 12.33

( years 28 - 100 ) 1.00 -18.33

($/ha) ($/tonne LC avoided CE)

CO2 Sink Enhancement, NPV Basis ( years 1 - 100 ) 305.75 87.66

Cropland: Two-Year Corn/Soybean Rotation

 Summary

Indiana

LC Avoided CE

(tonne/ha)

3.49

(tonne/ha/yr)

0.530

0.335

0.081

-0.055

-8

-6

-4

-2

0

2

4

6

8

0 20 40 60 80 100

Years

Avoided Emissions (tonneCE/ha) 

SOC Non-SOC

Total Avoided GHG Discounted Total Avoided GHG

0

100

200

300

400

500

600

700

0 20 40 60 80 100

Years

Cost ($ha) 

Undiscounted Cost Discounted Cost
Corn/Soybean Rotations in the U.S. Corn Belt

19-22

19.3.4 Sensitivity Summary

Example spreadsheet sensitivity summary output is presented in Table 19-16. This output 

corresponds to the second column in Table 19-11 and presents costs assuming adoption incentive 

payments for 5, 10, 15, and 20 years. These costs are presented assuming that no-till is 

maintained for the full 100-year planning horizon or for only as long as adoption incentives are 

paid to the farmer, after which the land is converted back to intensive-till. This analysis assumes 

that when no-till is switched back to intensive-till, SOC levels decrease at a rate of 

1500 kg/ha/yr. A base case of incentive payments for 10 years was selected for comparing costs 

with other CO2 sink enhancement or storage options.
Corn/Soybean Rotations in the U.S. Corn Belt

19-23

Table 19-16

Corn/soybean example spreadsheet sensitivity summary output when changing from 

intensive-till to no-till and increasing N fertilizer use

Current Input Variable Values

Calculation Method for Sequestering CO2 Quadratic Plateau

Efficiency Index (Slope Factor) 53.1

SOC Increase to New Steady State (kg/ha) 7221

Time Until Steady State (years) 27

SOC Release Rate after no-till time period (tonne C/ha/yr) 1.5

Farmer Adoption Incentive to Reduce Tillage ($/ha/yr) $25.00

Transaction Costs $5.00

Transaction Time Period (years) 1

Monitoring Costs $1.00

Monitoring Time Period (years) 100

After-Tax Discount Rate 6.09%

Sensitivity Input

Farmer Adoption Incentive Time Period Viewed 5 years

10 years

15 years

20 years

Push the adjacent button to update the sensitivity summary costs below.

CO 2 Sink Enhancement Costs (100 year summation)

No-till Time Farmer Adoption Incentive NPV Basis

Period Time Period LC avoided basis

(years) (years) ($/tonne C equivalent)

5 5 827.87

10 10 289.15

15 15 197.32

20 20 162.98

100 5 36.16

100 10 58.57

100 15 75.25

100 20 87.66

Cropland: Two-Year Corn/Soybean Rotation

Indiana

Sensitivity Summary

Update

Values
Corn/Soybean Rotations in the U.S. Corn Belt

19-24

Examples of the large effects of N rate and N2O emissions on amount of GHG emissions avoided 

and cost per tonne of GHG avoided are presented in Table 19-17. The scenarios in Table 19-17 

are for switching from intensive-till to no-till in a corn/soybean rotation in the Corn Belt. No 

significant change in N2O emissions due switching from intensive-till to no-till is a possibility 

with good N management, especially on well-drained soils. The increases in N rate and N2O 

emissions in Table 19-17 due to switching from intensive-till to no-till are possibilities with poor 

N management. Amounts of GHG avoided range from 9.6 tonnes CE LC GHG avoided/ha with 

a best-case N rate and N2O scenario to -3.0 tonnes CE LC GHG avoided/ha for a worst-case 

scenario. Costs on an NPV basis range from $64/tonne CE LC GHG avoided with a best-case N 

rate and N2O scenario to $113/tonne CE LC GHG avoided for a worst-case scenario. 

Differences between the best-case and worst-case scenarios regarding amount of GHG avoided 

are very large because the annual effects are manifested over the entire 100-year planning 

horizon. In order for reducing tillage on corn/soybean rotations to be a viable option for 

avoiding GHG emissions, N fertilizer will have to be managed efficiently so that the amount of 

N fertilizer used and the N2O emissions are not significantly increased.

Table 19-17

Effects of increases in N rate and N2O emissions on amounts and costs of avoided GHG 

via switching from intensive-till to no-till in a Corn/Soybean rotation

ΔN rate ΔN2O Years 1-100 NPV basis, years 1-100

kg N/ha/yr kg/CE/ha/yr kg/CE/ha/yr

tonne CE LC 

GHG 

avoided/ha

tonne CE LC 

GHG 

avoided/ha

$/tonne CE 

LC GHG 

avoided1/

0 0 0 9.6 4.8 64 2/

0 0 30.3 6.5 4.3 72 3/

25 14.4 63.5 1.8 3.5 88 4/

50 28.8 96.6 -3.0 2.7 113 5/

1/

 Assumes incentive payments for 20 years.

2/

 Base case except for no change in N2O emissions due to switching to no-till.

3/

 Base case (see first column, Table 19-11).

4/

 Base case except for an increase in N rate due to switching from to no-till (see second

 column, Table 19-11).

5/

 Base case except for an additional increase in N rate due to switching from to no-till

 (see third column, Table 19-11).
Corn/Soybean Rotations in the U.S. Corn Belt

19-25

19.4 References

1 Brenner, J., K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, T. Kautza, K. Killian, J. 

Schuler and S. Williams. 2001. Quantifying the change in greenhouse gas emissions due to 

natural resource conservation practice application in Iowa. Final report to the Iowa 

Conservation Partnership. Colorado State University Natural Resource Ecology Laboratory 

and USDA Natural Resources Conservation Service, Fort Collins, CO, USA.

2 Smith, P., J. Brenner, K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, K. Killian, D. 

Lamm, J. Schuler and S. Williams. 2002. Quantifying the change in greenhouse gas 

emissions due to natural resource conservation practice application in Indiana. Final report 

to the Indiana Conservation Partnership. Colorado State University Natural Resource 

Ecology Laboratory and USDA Natural Resources Conservation Service, Fort Collins, CO, 

USA.

3 Eve, M.D., M. Sperow, K. Howerton, K. Paustian, and R.F. Follett. 2002. Predicted impact 

of management changes on soil carbon storage for each cropland region of the conterminous 

United States. J. Soil and Water Conservation. 57 (4) 196-204. 

4 West, Tristram O. and Wlfred M. Post. 2002. Soil organic carbon sequestration rates for 

crops with reduced tillage and enhanced rotation. Submitted to Soil Science Society of 

America Journal.

5 Langemeier, L.N. and R.K. Taylor. 1998. A look at machinery cost. Kansas Farm 

Management Guide, MF-842. Kansas State University, Department of Agricultural 

Economics.

6 West, Tristram O. and Gregg Marland. 2002. A synthesis of carbon sequestration, carbon 

emissions, and net carbon flux in agriculture: comparing tillage practices in the United 

States. Agriculture, Ecosystems, and Environment (in press)

7 Vitosh. M.L., J.W. Johnson, and D.B. Mengel. 2000. Tri-state fertilizer recommendations 

for corn, soybeans, wheat, and alfalfa. www.agry.purdue.edu/ext/forages/publications/ay9-

32.htm

8 Fox, R.H. and V.A. Bandel. 1986. Nitrogen utilization with no-tillage corn. p. 117-148. In 

(Milton A. Sprague and Glover B. Triplett, eds.) No-tillage and surface-tillage agriculture: 

the tillage revolution. John Wiley & Sons. New York. 

9 Thomas, Grant W. and Wilbur W. Frye. 1984. Fertilization and liming. P. 87-126. In 

(Ronald E. Phillips and Shirley H. Phillips, eds.) No-tillage agriculture: principles and 

practices. Van Nostrand Reinhold Company. New York.

10 Mengel, D.S. 1992. Fertilizing corn using conservation 

tillage.http://www.agcom.purdue.edu/AgCom/Pubs/AY/AY-268.html
Corn/Soybean Rotations in the U.S. Corn Belt

19-26

11 KY. 2001. 200-2001 lime and fertilizer 

recommendations.www.ca.uky.edu/agc/pubs/agr/agr1/agr1.htm

12 Bock, B.R., K.R. Kelley, and J.J. Meisinger. 1992. Predicting N fertilizer needs for corn in 

humid regions: summary and future directions. p. 115-127. In (B.R. Bock and K.R. Kelley 

(eds.) Predicting N fertilizer needs in humid regions. Bull. Y-226. National Fertilizer 

Development Center. TVA. Muscle Shoals, AL.

13 USDA. 1995. Economic Research Service Agricultural Handbook 712. 

http://www.ers.usda.gov/publications/ah712/ah712_3.pdf

14 Robertson, G. Phillip, Eldor A. Paul, and Richard R. Harwood. 2001. Greenhouse gases in 

intensive agriculture: contributions of individual gases to the radiative forcing of the 

atmosphere. Science: 289 (5486): 1922-1925.

15 Del Grosso, S.J. W.J. Parton, A.R. Mosier, M.D. Hartman, J. Brenner, D.S. Ojima, and D.S. 

Schimel. 2001. Simulated interaction of carbon dynamics and nitrogen trace gas fluxes 

using the DAYCENT . P. 303-332. In M.J. Shaffer, Liwang Ma, and S. Hansen (eds.) 

Modeling carbon and nitrogen dynamics for soil management. Lewis Publishers. 

Washington, D.C.

16 Mummey, Daniel L., Jeffrey L. Smith, and George Blum. 1998. Assessment of alternative 

soil management practices on N2O emissions from U.S. agriculture. Agriculture, 

Ecosystems, and Environment 70:79-87.

17 Mudahar, Mohinder S. and Travis P. Hignett. 1987. Energy requirements, technology, and 

resources in the fertilizer sector. p. 25-61. In Z.R. Helsel (Ed.) Energy in World Agriculture. 

Vol. 2. Elsevier. New York.

18 Achorn, Frank P., David G. Salladay, and Jeffery L. Greenhill. 1980. Effects of high costs 

on fertilizer production. P. 5-12. Situation 80, TVA Fertilizer Conference. Kansas City, MO.

19 EPA. 2000. Inventory of U.S. greenhouse gas emissions and sinks: 1990-1998. EPA 236-R001. p. 5-17 to 5-19.
20-1

20

CONTINUOUS CORN IN THE U.S. CORN BELT

20.1 Introduction

As indicated in Chapter 18, continuous corn is produced on a much smaller land area in the 

United States than is corn rotated with soybeans. However, the amount of carbon sequestered 

per hectare is significantly larger for continuous corn than for corn/soybean rotations and a larger 

adoption incentive is required for continuous corn than for a corn/soybean rotation. Base-case 

results for continuous corn are presented in Section 20.2. The base cases assume good N 

fertilizer management. Effects of poor N fertilizer management on amounts and costs of GHG 

credits from continuous corn are presented in Section 20.3. Effects of N fertilizer management 

are greater for continuous corn than for corn/soybean rotations because N fertilizer is used every 

year for continuous corn and only every other year for corn/soybean rotations.

20.2 Continuous Corn (Base Cases)

A summary of effects of switching from intensive-till to no- or moderate-till on SOC, GHG 

emissions, and costs of avoided GHG emissions is presented in Table 20-1 for continuous corn in 

Indiana and Iowa. The bases for the results in Table 20-1 are discussed in the following sections 

using output from the continuous corn spreadsheet model developed for this project.
Continuous Corn in the U.S. Corn Belt

20-2

Table 20-1

Effects of switching from intensive-till to no- or moderate-till on SOC, GHG emissions, and 

costs of life-cycle (LC) avoided GHG emissions--Indiana and Iowa state averages for 

Continuous Corn

State Indiana Indiana Iowa Iowa

Switching from intensive-till to: No-till

Moderatetill No-tillModeratetill

Quadratic SOC response curve

 ΔSOCs, kg C/ha 13104 10174

1335

5 5619

 E (slope factor) 92.0 72.5 92.3 35.3

 Years to new steady state (Ys) 28 28 29 32

Average annual increase in SOC (years 1 to Ys), 

kg C/ha/yr 468 363 461 176

GHG emissions from tillage-system inputs and N2O (years 1-100), kg CE/ha/y:

 Δ Fuel use -22.9 -20.5 -22.9 -20.5

 Δ Machinery maint., trans., and repair -4 -4 -4 -4

 Δ N fertilizer use 0 0 0 0

 Δ Herbicide use 4 1.2 4 1.2

 Δ Total GHG emissions from inputs -22.7 -23.0 -22.7 -23.0

 Δ N2O emissions from soil 30.3 30.3 30.3 30.3

 Δ Total GHG emissions 7.6 7.3 7.6 7.3

Average LC GHG avoided (kg CE/ha/y)

 Years 1 to Ys 460 356 453 168

 Years Ys to 100 -8 -7 -8 -7

Total LC GHG avoided (years 1-100), tonne 

CE/ha 12.3 9.4 12.6 4.9

NPV Total LC GHG avoided (years 1-100),

tonne CE/ha 7.7 6.0 7.8 3.1

Costs, $/ha/y

 Transaction (year 1) 5 5 5 5

 Adoption incentive (years 1-20) 50 25 50 25

 Monitoring (years 1-100) 1 1 1 1

Total costs (years 1-100), $/ha 1105 605 1105 605

NPV Total costs (years 1-100), $/ha 590 306 590 306

Cost (NPV basis), $/tonne CE LC GHG avoided 77 51 76 100
Continuous Corn in the U.S. Corn Belt

20-3

20.2.1 Increases in Soil Organic Carbon

The increases in SOC due to switching from intensive-till to no-till or moderate-till (Table 20-1) 

are based on state- and county-level assessments for Iowa1 and Indiana2. Iowa and Indiana form 

a representative east-west transect across the mid-section of the Corn Belt states. Similar 

assessments are not available for the other Corn Belt states. The Iowa and Indiana assessments 

provide county-level estimates of increases in SOC for 10 and 20 years after switching 

corn/soybean rotations from intensive-till to no-till or moderate-till. These results were provided 

for several combinations of soil texture and internal drainage [non-hydric (i.e., well drained) or 

hydric (i.e., poorly drained)]. State-level estimates of increases in SOC were provided for 

10 years after switching from intensive-till to no-till or moderate-till on both non-hydric and 

hydric soils. SOC response curves for counties representing the range and mean of SOC 

responses in Indiana and Iowa are presented in Appendix F. An interpolation procedure 

described in Appendix F was used to develop state-level SOC response curves for switching 

from intensive-till to no-till and moderate-till in Indiana and Iowa. Graphs of the four SOC 

response curves represented in Table 20-1 are presented in Figure 20-1. After completing 

county-level carbon storage estimates first in Iowa1 and then in Indiana2, results from Indiana 

were judged to be most representative of the Corn Belt (personal communication, John Brenner, 

NRCS).

IA and IN State Curves--Cont. Corn

0.0

2000.0

4000.0

6000.0

8000.0

10000.0

12000.0

14000.0

0 10 20 30 40

Years after switching tillage

Increase in SOC, kg/ha

IA-NT

IN-NT

IA-MT

IN-MT

Figure 20-1

State SOC response curves for switching from intensive-till to no-till (NT) or moderate-till 

(MT) in Iowa and Indiana
Continuous Corn in the U.S. Corn Belt

20-4

Example spreadsheet inputs for calculating increases in SOC due to switching from intensive-till 

to no-till in Indiana (first column of Table 20-1) are presented in Table 20-2.

Table 20-2

Example process and economic input variables for switching from intensive-till to no-till 

for continuous corn in Indiana

Process

Alternative Tillage System:

Calculation Method for Sequestering Soil Organic Carbon (SOC)

Quadratic Plateau

Linear Plateau

Efficiency or Slope Index (E) 92.0

SOC Increase to New Steady State (ΔSOCs) 13103.5

Years until New Steady State (Ys) 28 years

No-till time period 100 years

SOC Release Rate after no-till time period 1500 kg C/ha/yr

Economic

Cost

$/ha/yr

Transaction Costs ( years 1 - 1 ) 5.00

Farmer Adoption Incentive to Reduce Tillage ( years 1 - 20 ) 50.00

Monitoring Costs ( years 1 - 100 ) 1.00

Discount rate 6.09%

Cropland: Continuous Corn

Process and Economic Input Variables

(Changes Due to Switching from Intensive-Till to No-Till)

Indiana

No-till

 
Continuous Corn in the U.S. Corn Belt

20-5

20.2.2 Costs

Example cost inputs are presented in Table 20-2. Experience with conservation-tillage adoption 

programs indicates that an adoption incentive of at least $50/ha/year for continuous corn will be 

required to achieve additional adoption from intensive-till of no-till in the Corn Belt (personal 

communication, Dan Towery, Conservation Tillage Information Center). For example, the 

Environmental Quality Incentives Program (EQIP) in Illinois is providing an incentive of 

$50/ha/yr for switching corn from intensive tillage to no tillage and is providing no incentive for 

switching soybeans from intensive tillage to no tillage. Conservation-tillage adoption incentives 

are estimated in Appendix E and ranged from $66 to 100/ha/year for switching from intensivetill to no-till continuous corn. Risks of lower and more variable yields associated with no-till 

continuous corn have been lowered somewhat in recent years via improvements in technology 

and experience with no-till. As a first approximation, we assume an adoption incentive of 

$50/ha/yr will be required to achieve additional adoption of continuous corn from intensive-till 

to no-till and that an adoption incentive of $25/ha/yr will be required to achieve additional 

adoption of continuous corn from intensive-till to moderate-till. Transaction costs are based on 

experience with forestry projects and are estimated at $5/ha. Monitoring costs are assumed to be 

less than for forestry ($5/ha), assuming that cropland monitoring will be based mainly on 

monitoring practices that have been correlated with changes in SOC rather than direct 

measurements of SOC in each field. Monitoring costs of $1/ha/year were assumed.

20.2.3 GHG Effects in Addition to Carbon Storage in Soil Organic Matter

20.2.3.1 Fuel Use

Reductions in fuel use and associated carbon emissions by switching from intensive-till to no-till 

in a corn/soybean rotation are presented in Table 20-3. Fuel usage rates are from Langemeier 

and Taylor3. Reductions in fuel use and associated carbon emissions by switching from 

intensive-till to moderate-till for continuous corn are presented in Table 20-4. Fuel usage rates 

are from Langemeier and Taylor3.
Continuous Corn in the U.S. Corn Belt

20-6

Table 20-3

Reductions in fuel use and associated carbon emissions by switching from intensive-till to 

no-till in continuous corn

gal./acre kg C/ha

Intensive-till:

Disk corn stalks 0.45 3.6

Moldboard plow 1.68 13.3

Disk 0.55 4.4

Field cultivate 0.60 4.8

Plant 0.50 4.0

Cultivate 0.45 3.6

Total 4.23 33.6

No-till:

Burndown herbicide application 0.10 0.8

Shred Corn Stalks 0.75 6.0

Plant 0.50 4.0

Total 1.35 10.7

Tillage-system difference ( years 1- 100 ) -22.9 kg C/ha

Annual fuel saving from reducing tillage -22.9 kg C/ha

Annual machinery MTR savings 18% of fuel savings

Annual machinery MTR saving from reducing tillage ( years 1- 100 ) -4.1 kg C/ha

Indiana

Cropland: Continuous Corn

Fuel Use for tillage, planting, and herbicide operations

Energy Savings Embodied in Machinery Manufacturing, 

15.71

5.14

5.61

4.68

Transportation, & Repair (MTR)

4.21

7.01

Annual Fuel Use

4.68

12.62

L/ha

4.21

39.55

0.94

20.2.3.2 Machinery Manufacture, Transportation, and Repair

Reductions in machinery manufacture, transportation, and repair (MTR) and associated carbon 

emissions by switching from intensive-till to no-till or moderate-till for continuous corn were 

calculated as 18 percent of the fuel savings (Tables 20-3 and 20-4). The 18 percent factor is 

based on ratios of tillage-system fuel use and machinery MTR in a national analysis of tillagesystem effects on GHG emissions4

.
Continuous Corn in the U.S. Corn Belt

20-7

Table 20-4

Reductions in fuel use and associated carbon emissions by switching from intensive-till to 

moderate-till in continuous corn

gal./acre kg C/ha

Intensive-till:

Disk corn stalks 0.45 3.6

Moldboard plow 1.68 13.3

Disk 0.55 4.4

Field cultivate 0.60 4.8

Plant 0.50 4.0

Cultivate 0.45 3.6

Total 4.23 33.6

Moderate-till:

Disk 0.55 4.4

Field Cultivate 0.60 4.8

Plant 0.50 4.0

Total 1.65 13.1

Tillage-system difference ( years 1- 100 ) -20.5 kg C/ha

Annual fuel saving from reducing tillage -20.5 kg C/ha

Annual machinery MTR savings 18% of fuel savings

Annual machinery MTR saving from reducing tillage ( years 1- 100 ) -3.7 kg C/ha

Indiana

Cropland: Continuous Corn

Fuel Use for tillage, planting, and herbicide operations

Energy Savings Embodied in Machinery Manufacturing, 

15.71

5.14

5.61

4.68

Transportation, & Repair (MTR)

4.21

5.61

Annual Fuel Use

4.68

15.43

L/ha

4.21

39.55

5.14

20.2.3.3 N Fertilizer Use

Effects of reducing tillage on the amount of N fertilizer required for corn was discussed in 

Section 19.2.3.3. The conclusion was that when N is injected well below the decomposing crop 

residues and is applied close to when the crop needs the N, then the amount of N fertilizer 

required for Corn in the Corn Belt should not be significantly affected by tillage system. 

However, with poorly managed N, more N fertilizer will be required with conservation tillage 

than with intensive tillage. The base case for continuous corn summarized in Table 20-1 

assumes that the amount of N fertilizer used in not affected by tillage system. Effects of 

applying more N fertilizer with no-till than with intensive-till, a case that is applicable when 

N fertilizer is poorly managed, is presented in Section 20-3.
Continuous Corn in the U.S. Corn Belt

20-8

20.2.3.4 Herbicide Use

The mean annual herbicide rates for intensive-till and no-till continuous corn were estimated 

from West and Marland

4

 as 2.71 and 3.63 kg active ingredient (a.i.)/ha, respectively. West and 

Marland4 estimated 4.70 kg C emitted/kg herbicide a.i. used. With these factors, carbon 

emissions were 4.3 kg C/ha/yr more for no-till than intensive-till. The mean annual herbicide 

rate for moderate-till corn/soybean rotations was estimated from West and Marland4 as 2.96 kg 

a.i./ha. Multiplying (2.96-2.71 kg a.i./ha) by 4.70 kg C emitted/kg herbicide gave 1.2 kg C/ha/yr 

more for moderate-till than no-till. Example spreadsheet output is presented in Table 20-5.

Table 20-5

Example spreadsheet output for estimating herbicide carbon equivalent emissions for 

continuous corn

Mean annual

herbicide rate

kg a.i./ha kg C/kg herbicide (a.i.) kg C/ha/yr

Intensive-till continuous corn 2.71

No-till continuous corn 3.63

Difference ( years 1- 100 ) 0.92 4.70 4.3

Cropland: Continuous Corn

Annual Herbicide Use X Tillage System

Indiana

20.2.3.5 N2O Emissions from Soil

Background on N2O emissions from soil was provided earlier, indicating that N2O emissions are 

dependent on both soil conditions and amount of N fertilizer applied. In this Corn Belt 

continuous corn case, the amount of N fertilizer applied does not change with tillage system, but 

soil conditions do change as discussed earlier. Robertson et al.5 measured effects of switching 

from intensive-till to no-till in a corn/wheat/soybean rotation on a well drained soil in Michigan. 

The same N rate was used for both tillage systems and anhydrous ammonia was the primary 

N source. Switching to no-till increased N2O emissions from 141 to 152 kg CE/ha/yr. Del 

Grosso et al.6 modeled N2O emissions as affected by switching corn/soybean rotations from 

intensive-till to no-till on a well drained soil in Indiana. The same N fertilizer rate was assumed 

for both tillage systems. A specific placement (e.g., knifing or broadcast) of the N fertilizer was 

not indicated. With these assumptions, N2O emissions were slightly lower with no-till than 

intensive-till in both a corn/soybean rotation and continuous corn. Mummey et al.7 modeled N2O 

emissions for intensive-till and no-till systems at 1035 sites for corn and 655 sites for soybeans. 

These sites represented the range of soil and environmental conditions for these crops in the 

United States. This modeling effort indicated that in the north central U.S. the mean N2O-N 

emissions were 3.04 kg/ha for intensive-till and 3.28 kg/ha for no-till. Taking the difference 

(0.24 kg N2O-N/ha) times 126.4 kg N2O CE/kg N2O-N gives 30.3 kg N2O CE/ha/yr. This is a 

larger increase in N2O emissions due to switching to no-till than was found by Robertson et al.5
Continuous Corn in the U.S. Corn Belt

20-9

and Del Grosso et al.6, but the latter two studies were on well drained soils whereas the Mummey 

et al.7 study covered a range of soil and environmental conditions. On the other hand, Mummey 

et al.7 did not account for possible reductions in denitrification and N2O emissions with knifed N 

fertilizer applications that are common with anhydrous ammonia in the Corn Belt. The 

Mummey et al.7 estimates of increased N2O emissions due to switching from intensive-till to notill in the north central U.S. were used as inputs to generate results in Table 20.1 for the 

continuous corn Corn Belt base case. Example spreadsheet output is presented in Table 20-6.

Table 20-6

Example spreadsheet output for carbon equivalent emissions from changes in N2O 

emissions for continuous corn when changing from intensive-till to no-till

Annual

kg N2O-N/ha kg N2O CE/kg N2O-N kg N2O CE/ha/yr

Intensive-till continuous corn 3.04

No-till continuous corn 3.28

Difference ( years 1- 100 ) 0.24 126.4 30.3

Mean annual

N rate IPCC emission factor

kg N/ha kg N2O CE /kg N applied kg N2O CE/ha/yr

Intensive-till continuous corn 150.0

No-till continuous corn 150.0

Difference ( years 1- 100 ) 0.0 2.65 0.0

Total Difference ( years 1- 100 ) 30.3

Associated with Changes in N Application Rate

Due to Tillage-Related Changes in Soil Properties & Micro-Climate

Cropland: Continuous Corn

Annual Changes in N2O Emissions

Indiana
Continuous Corn in the U.S. Corn Belt

20-10

20.2.3.6 Summary of Avoided GHG Emissions

Example spreadsheet output summarizing changes in GHG emissions is presented in Table 20-7. 

This output corresponds to the first column in Table 20-1.

Table 20-7

Example spreadsheet output summarizing changes in GHG emissions for continuous corn 

when changing from intensive-till to no-till

Changes in GHG Emissions

kg CE/ha/yr

Average Soil Organic Carbon (SOC) sequestered ( years 1 - 28 ) -468

CO2 Emissions from Tillage-System Inputs: ( years 1 - 100 )

Fuel -22.9

Machinery MTR -4.1

N fertilizer 0.0

Herbicides 4.3

Total -22.7

N2O Emissions from Soil ( years 1 - 100 ) 30.3

Avoided GHG Emissions

kg CE/ha/yr tonne CE/ha/yr

( years 1 - 28 ) 460.2 0.460

( years 29 - 100 ) -7.7 -0.008

Annual Average

Cropland: Continuous Corn

Life Cycle (LC) Avoided GHG Emissions

(Due to Switching from Intensive-Till to No-Till)

Indiana

20.2.4 Summary

Example spreadsheet output summarizing costs and life-cycle (LC) avoided GHG on a carbon 

equivalent (CE) basis is presented in Table 20-8. This output corresponds to the first column in 

Table 20-1.
Continuous Corn in the U.S. Corn Belt

20-11

Table 20-8

Costs and life-cycle (LC) avoided GHG on a carbon equivalent (CE) basis for continuous 

corn when changing from intensive-till to no-till

Process Description

Calculation Method for Sequestering SOC -Quadratic Plateau No-till time period 100 years

Average SOC Sequestered 468 kg C/ha/yr Release Rate after No-till 1.5 tonne CE/ha/yr

Time until Steady State 28 years

Economic

Cost

$/ha/yr $/tonne LC avoided CE

Average Annual Costs ( years 1 - 1 ) 56.00 60.44

( years 2 - 20 ) 51.00 84.50

( years 21 - 28 ) 1.00 5.97

( years 29 - 100 ) 1.00 -130.16

($/ha) ($/tonne LC avoided CE)

CO2 Sink Enhancement, NPV Basis ( years 1 - 100 ) 590.42 76.95

Cropland: Continuous Corn

 Summary

Indiana

LC Avoided CE

tonne/ha/yr

0.927

0.604

0.168

(tonne/ha)

7.67

-0.008

-2

0

2

4

6

8

10

12

14

0 10 20 30 40 50 60 70 80 90 100

Years

Avoided Emissions (tonne CE/ha) 

SOC Non SOC Total Avoided GHG Discounted Total Avoided GHG

0

200

400

600

800

1000

1200

0 10 20 30 40 50 60 70 80 90 100

Years

Cost ($ha) 

Undiscounted Cost Discounted Cost
Continuous Corn in the U.S. Corn Belt

20-12

20.2.5 Sensitivity Summary

Example spreadsheet sensitivity summary output is presented in Table 20-9. This sensitivity 

output presents costs assuming adoption incentive payments for 5, 10, 15, and 20 years. These 

costs are presented assuming that no-till is maintained for the full 100-year planning horizon or 

for only as long as adoption incentives are paid to the farmer, after which the land is converted 

back to intensive-till. This analysis assumes that when no-till is switched back to intensive-till, 

SOC levels decrease at a rate of 1500 kg/ha/yr. A base case of incentive payments for 10 years 

was selected for comparing costs with other CO2 sink enhancement or storage options.
Continuous Corn in the U.S. Corn Belt

20-13

Table 20-9

Continuous corn sensitivity summary

Current Input Variable Values

Calculation Method for Sequestering CO2 Quadratic Plateau

Efficiency Index (Slope Factor) 92

SOC Increase to New Steady State (kg/ha) 13103.5

Time Until Steady State (years) 28

SOC Release Rate after no-till time period (tonne C/ha/yr) 1.5

Farmer Adoption Incentive to Reduce Tillage ($/ha/yr) $50.00

Transaction Costs $5.00

Transaction Time Period (years) 1

Monitoring Costs $1.00

Monitoring Time Period (years) 100

After-Tax Discount Rate 6.09%

Sensitivity Input

Farmer Adoption Incentive Time Period Viewed 5 years

10 years

15 years

20 years

Push the adjacent button to update the sensitivity summary costs below.

CO 2 Sink Enhancement Costs ( years 1-100 )

No-till Time Farmer Adoption Incentive NPV Basis

Period Time Period LC avoided GHG basis

(years) (years) ($/tonne C equivalent)

5 5 320.60

10 10 142.87

15 15 145.55

20 20 128.82

100 5 30.13

100 10 50.51

100 15 65.67

100 20 76.95

Cropland: Continuous Corn

Sensitivity Summary

Indiana
Continuous Corn in the U.S. Corn Belt

20-14

20.3 Continuous Corn (Poor N Management Scenario)

This case is the same as the Indiana no-till base case in the last section except that poorer 

N management (broadcast application of urea) is assumed, resulting in more N fertilizer being 

required with no-till corn than intensive-till corn. Using more N fertilizer increases GHG 

emissions associated with N fertilizer use and also increases N2O emissions. This case assumes 

150 kg N/ha for intensive-till and 175 kg N/ha or 200 kg/ha for no-till. These N rates fall within 

the recommended rates for intensive-till and no-till corn in Kentucky8.

Table 20-10

Effects of switching from intensive-till Using 150 kg N/ha to no- till Using 175 or 200 kg 

N/ha on GHG emissions and costs of life-cycle (LC) avoided GHG emissions—Indiana 

Continuous Corn 

Switching from intensive-till to: No-till No-till No-till

N rate, kg/ha 150 175 200

Quadratic SOC response curve

 ΔSOCs, kg C/ha 13104 13104 13104

 E (slope factor) 92.0 92.0 92.0

 Years to new steady state (Ys) 28 28 28

 Average annual increase in SOC (years 1 to Ys), kg C/ha/yr

468 468 468

GHG emissions from tillage-system inputs and N2O (years 1-100), kg CE/ha/y:

 Δ Fuel use -22.9 -22.9 -22.9

 Δ Machinery maintenance, trans., and repair -4 -4 -4

 Δ N fertilizer use 0 21.0 57.4

 Δ Herbicide use 4 4 4

 Δ Total GHG emissions from inputs -22.7 6.0 34.7

 Δ N2O emissions from soil 30.3 96.6 162.3

 Δ Total GHG emissions 7.6 102.6 197.0

Average LC GHG avoided (kg CE/ha/y)

 Years 1 to Ys 460 365 270

 Years Ys to 100 -8 -103 -198

Total LC GHG avoided (years 1-100), tonne CE/ha 12.3 2.8 -6.7

NPV Total LC GHG avoided (years 1-100), tonne 

CE/ha 7.7 6.1 4.6

Costs, $/ha/y

 Transaction (year 1) 5 5 5

 Adoption incentive (years 1-20) 50 50 50

 Monitoring (years 1-100) 1 1 1

Total costs (years 1-100), $/ha 1105 1105 1105

NPV Total costs (years 1-100), $/ha 590 590 590

Cost (NPV basis), $/tonne CE LC GHG avoided 77 97 129
Continuous Corn in the U.S. Corn Belt

20-15

A summary of results is presented in Table 20-10. Increasing the N rate every year for corn by 

25 kg N/ha increased the cost of avoided GHG from $77 to $97/tonne CE LC avoided. 

Increasing the N rate for corn by 50 kg N/ha increased the cost of avoided GHG from $77/tonne 

CE LC avoided to $129/tonne CE LC avoided. These increased costs are due to less avoidance 

of GHG emissions per dollar invested in adoption incentives and transaction and monitoring 

costs. Adding an additional 50 kg N/ha every year for corn nearly doubled the cost per tonne 

CE LC avoided on an NPV basis and resulted in a negative avoidance of GHG emissions on a 

nondiscounted basis after 100 years (a decrease from 12.3 to -6.7 tonnes CE LC avoided). This 

emphasizes the importance of efficient N fertilizer management in cropping systems designed to 

reduce GHG emissions. Details of the effects of increased N fertilizer use on GHG emissions 

from N fertilizer manufacture, transportation, storage, and application and on N2O emissions are 

presented in the next two sections.

20.3.1 N Fertilizer Use

Carbon emissions associated with important combinations of N source and placement are 

presented in Table 20-11. Emissions from N fertilizer use include manufacture9 and 

transportation and storage10. Emissions from N fertilizer application are from Langemeier and 

Taylor3. The manufacturing numbers assume that all energy is from natural gas, but a small 

undermined amount of energy is from electricity. The transportation, storage, and application 

numbers assume the energy is from diesel fuel.

Table 20-11

Continuous corn carbon emissions associated with important combinations of N source 

and placement

N Appln. Total

Source/Placement kg N/ha kg C/kg N kg C/ha kg C/ha kg C/ha

Intensive-till:

Anhydrous ammonia/knifed 0.0 0.838 0.0 0 0.0

Urea/broadcast 150.0 1.148 172.2 1.6 173.8

UAN solution/broadcast 0.0 1.012 0.0 0.0 0.0

UAN solution/knifed 0.0 1.012 0.0 0.0 0.0

Ammonium nitrate/broadcast 0.0 1.021 0.0 0.0 0.0

Total 150.0 172.2 1.6 173.8

No-till:

Anhydrous ammonia/knifed 0.0 0.838 0.0 0 0.0

Urea/broadcast 175.0 1.148 200.9 1.6 202.5

UAN solution/broadcast 0.0 1.012 0.0 0.0 0.0

UAN solution/knifed 0.0 1.012 0.0 0.0 0.0

Ammonium nitrate/broadcast 0.0 1.021 0.0 0.0 0.0

Total 175.0 200.9 1.6 202.5

Tillage-system difference ( years 1- 100 ) 25.0 28.7 28.7

Cropland: Continuous Corn

Annual N fertilizer use

N Fertilizer Use X Crop and Tillage System

Indiana
Continuous Corn in the U.S. Corn Belt

20-16

This case assumes that urea is broadcast for corn in both the intensive-till and no-till systems and 

that 150 kg N/ha and 175 kg N/ha are required for intensive-till and no-till corn , respectively. 

This resulted in an annual increase in N fertilizer use of 25 kg N/ha.

The increase in N fertilizer use with no-till was assumed to increase N2O emissions from soils in 

accordance with the IPCC default N2O emission factor for commercial N fertilizer use11. The 

IPCC factor assumes the following:

• Direct N2O-N emissions = 1.25 percent of applied N

• Indirect N2O-N emissions from:

Volatilized NH3 and NOx = 0.1 percent of applied N

Leached N = 0.75 percent of applied N

• Total N2O-N emission = 2.1 percent of applied N

(i.e., 0.021 kg N2O-N/kg applied N)

The IPCC factor is converted to a carbon equivalent (CE) radiative forcing basis as follows:

• 1.57 N2O/N2O-N 

• 296 CO2 equivalent/N2O

• 0.272 C equivalent (CE)/CO2 equivalent

• 1.57 x 296 x .272 CE/N2O-N

(126.4 CE/N2O-N)

Therefore, 1 kg applied N = 0.021 x 126.4 kg CE from N2O

 = 2.65 kg CE from N2O

The overall increase in N2O emissions due to both switching from intensive-till to no-till and 

increasing N fertilizer use was calculated as shown in Table 20-12. Increased N2O emissions 

due to tillage-related changes in soil properties and micro-climate (without changes in 

N fertilizer rate) are based on the same assumptions as in the previous case in which N fertilizer 

rate didn’t change with tillage system.
Continuous Corn in the U.S. Corn Belt

20-17

Table 20-12

Continuous corn overall increase in N2O emissions due to both switching from intensivetill to no-till and increasing N fertilizer use

Annual

kg N2O-N/ha kg N2O CE/kg N2O-N kg N2O CE/ha/yr

Intensive-till continuous corn 3.04

No-till continuous corn 3.28

Difference ( years 1- 100 ) 0.24 126.4 30.3

Mean annual

N rate IPCC emission factor

kg N/ha kg N2O CE /kg N applied kg N2O CE/ha/yr

Intensive-till continuous corn 150.0

No-till continuous corn 175.0

Difference ( years 1- 100 ) 25.0 2.65 66.3

Total Difference ( years 1- 100 ) 96.6

Associated with Changes in N Application Rate

Due to Tillage-Related Changes in Soil Properties & Micro-Climate

Cropland: Continuous Corn

Annual Changes in N2O Emissions

Indiana

20.3.2 Summary of Avoided GHG Emissions

Example spreadsheet output summarizing changes in GHG emissions due to switching from 

intensive-till to no-till and increasing N fertilizer use by 25 kg N/ha is presented in Table 20-13. 

These results correspond to the second column in Table 20-10. Over the course of the 100-year 

planning horizon, the added N fertilizer use and associated increase in N2O emissions 

substantially reduced the amount of GHG emissions avoided (Table 20-14).
Continuous Corn in the U.S. Corn Belt

20-18

Table 20-13

Continuous sorn example spreadsheet output summarizing changes in GHG emissions 

due to switching from intensive-till to no-till and increasing N fertilizer use

Changes in GHG Emissions

kg CE/ha/yr

Average Soil Organic Carbon (SOC) sequestered ( years 1 - 28 ) -468

CO2 Emissions from Tillage-System Inputs: ( years 1 - 100 )

Fuel -22.9

Machinery MTR -4.1

N fertilizer 28.7

Herbicides 4.3

Total 6.0

N2O Emissions from Soil ( years 1 - 100 ) 96.6

Avoided GHG Emissions

kg CE/ha/yr tonne CE/ha/yr

( years 1 - 28 ) 365.2 0.365

( years 29 - 100 ) -102.6 -0.103

Annual Average

Cropland: Continuous Corn

Life Cycle (LC) Avoided GHG Emissions

(Due to Switching from Intensive-Till to No-Till)

Indiana

20.3.3 Summary

Example spreadsheet output summarizing costs and life-cycle (LC) avoided GHG on a carbon 

equivalent (CE) basis is presented in Table 20-14. This output corresponds to the second column 

in Table 20-10.
Continuous Corn in the U.S. Corn Belt

20-19

Table 20-14

Continuous corn costs and life-cycle (LC) avoided GHG on a carbon equivalent (CE) basis 

when changing from intensive-till to no-till and increasing N fertilizer use

Process Description

Calculation Method for Sequestering SOC -Quadratic Plateau No-till time period 100 years

Average SOC Sequestered 468 kg C/ha/yr Release Rate after No-till 1.5 tonne CE/ha/yr

Time until Steady State 28 years

Economic

Cost

$/ha/yr $/tonne LC avoided CE

Average Annual Costs ( years 1 - 1 ) 56.00 62.37

( years 2 - 20 ) 51.00 88.72

( years 21 - 28 ) 1.00 7.20

( years 29 - 100 ) 1.00 -9.74

($/ha) ($/tonne LC avoided CE)

CO2 Sink Enhancement, NPV Basis ( years 1 - 100 ) 590.42 96.51

Cropland: Continuous Corn

 Summary

Indiana

LC Avoided CE

tonne/ha/yr

0.898

0.575

0.139

(tonne/ha)

6.12

-0.103

-15

-10

-5

0

5

10

15

0 10 20 30 40 50 60 70 80 90 100

Years

Avoided Emissions (tonne CE/ha) 

SOC Non SOC Total Avoided GHG Discounted Total Avoided GHG

0

200

400

600

800

1000

1200

0 10 20 30 40 50 60 70 80 90 100

Years

Cost ($ha) 

Undiscounted Cost Discounted Cost
Continuous Corn in the U.S. Corn Belt

20-20

20.3.4 Sensitivity Summary

Example spreadsheet sensitivity summary output is presented in Table 20-15. This output 

corresponds to the second column in Table 20-10 and presents costs assuming adoption incentive 

payments for 5, 10, 15, and 20 years. These costs are presented assuming that no-till is 

maintained for the full 100-year planning horizon or for only as long as adoption incentives are 

paid to the farmer, after which the land is converted back to intensive-till. This analysis assumes 

that when no-till is switched back to intensive-till, SOC levels decrease at a rate of 

1500 kg/ha/yr. A base case of incentive payments for 10 years was selected for comparing costs 

with other CO2 sink enhancement or storage options.
Continuous Corn in the U.S. Corn Belt

20-21

Table 20-15

Continuous corn example spreadsheet sensitivity summary output when changing from 

intensive-till to no-till and increasing N fertilizer use

Current Input Variable Values

Calculation Method for Sequestering CO2 Quadratic Plateau

Efficiency Index (Slope Factor) 92

SOC Increase to New Steady State (kg/ha) 13103.5

Time Until Steady State (years) 28

SOC Release Rate after no-till time period (tonne C/ha/yr) 1.5

Farmer Adoption Incentive to Reduce Tillage ($/ha/yr) $50.00

Transaction Costs $5.00

Transaction Time Period (years) 1

Monitoring Costs $1.00

Monitoring Time Period (years) 100

After-Tax Discount Rate 6.09%

Sensitivity Input

Farmer Adoption Incentive Time Period Viewed 5 years

10 years

15 years

20 years

Push the adjacent button to update the sensitivity summary costs below.

CO 2 Sink Enhancement Costs ( years 1-100 )

No-till Time Farmer Adoption Incentive NPV Basis

Period Time Period LC avoided GHG basis

(years) (years) ($/tonne C equivalent)

5 5 717.68

10 10 192.17

15 15 197.99

20 20 168.59

100 5 37.79

100 10 63.35

100 15 82.36

100 20 96.51

Cropland: Continuous Corn

Sensitivity Summary

Indiana
Continuous Corn in the U.S. Corn Belt

20-22

Examples of the large effects of N rate and N2O emissions on amount of GHG emissions avoided 

and cost per tonne of GHG avoided are presented in Table 20-16. The scenarios in Table 20-16 

are for switching from intensive-till to no-till continuous corn in the Corn Belt. No significant 

change in N2O emissions due switching from intensive-till to no-till is a possibility with good 

N management, especially on well-drained soils. The increases in N rate and N2O emissions in 

Table 20-16 due to switching from intensive-till to no-till are possibilities with poor 

N management. Amounts of GHG avoided range from 15.4 tonnes CE LC GHG avoided/ha 

with a best-case N rate and N2O scenario to -6.7 tonnes CE LC GHG avoided/ha for a worst-case 

scenario. Costs on an NPV basis range from $72/tonne CE LC GHG avoided with a best-case 

N rate and N2O scenario to $129/tonne CE LC GHG avoided for a worst-case scenario. 

Differences between the best-case and worst-case scenarios regarding amount of GHG avoided 

are very large because the annual effects are manifested over the entire 100-year planning 

horizon. In order for reducing tillage on continuous corn to be a viable option for avoiding GHG 

emissions, N fertilizer will have to be managed efficiently so that the amount of N fertilizer used 

and the N2O emissions are not significantly increased.

Table 20-16

Effects of increases in N rate and N2O emissions on amounts and costs of avoided GHG 

via switching from intensive-till to no-Continuous Corn

ΔN rate ΔN2O Years 1-100 NPV basis, years 1-100

kg N/ha/yr kg/CE/ha/yr kg/CE/ha/yr

tonne CE LC 

GHG 

avoided/ha

tonne CE LC 

GHG 

avoided/ha

$/tonne CE 

LC GHG 

avoided1/

0 0 0 15.4 8.2 72 2/

0 0 30 12.3 7.7 77 3/

25 29 97 2.8 6.7 97 4/

50 58 163 -6.7 4.7 129 5/

1/

 Assumes incentive payments for 20 years.

2/

 Base case except for no change in N2O emissions due to switching to no-till.

3/

 Base case (see first column, Table 20-10).

4/

 Base case except for an increase in N rate due to switching from to no-till (see second

 column, Table 20-10).

5/

 Base case except for an additional increase in N rate due to switching from to no-till

 (see third column, Table 20-10).
Continuous Corn in the U.S. Corn Belt

20-23

20.4 References

1 Brenner, J., K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, T. Kautza, K. Killian, J. 

Schuler and S. Williams. 2001. Quantifying the change in greenhouse gas emissions due to 

natural resource conservation practice application in Iowa. Final report to the Iowa 

Conservation Partnership. Colorado State University Natural Resource Ecology Laboratory 

and USDA Natural Resources Conservation Service, Fort Collins, CO, USA.

2 Smith, P., J. Brenner, K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, K. Killian, D. 

Lamm, J. Schuler and S. Williams. 2002. Quantifying the change in greenhouse gas 

emissions due to natural resource conservation practice application in Indiana. Final report 

to the Indiana Conservation Partnership. Colorado State University Natural Resource 

Ecology Laboratory and USDA Natural Resources Conservation Service, Fort Collins, CO, 

USA.

3 Langemeier, L.N. and R.K. Taylor. 1998. A look at machinery cost. Kansas Farm 

Management Guide, MF-842. Kansas State University, Department of Agricultural 

Economics.

4 West, Tristram O. and Gregg Marland. 2002. A synthesis of carbon sequestration, carbon 

emissions, and net carbon flux in agriculture: comparing tillage practices in the United 

States. Agriculture, Ecosystems, and Environment (in press)

5 Robertson, G. Phillip, Eldor A. Paul, and Richard R. Harwood. 2001. Greenhouse gases in 

intensive agriculture: contributions of individual gases to the radiative forcing of the 

atmosphere. Science: 289 (5486): 1922-1925.

6 Del Grosso, S.J. W.J. Parton, A.R. Mosier, M.D. Hartman, J. Brenner, D.S. Ojima, and D.S. 

Schimel. 2001. Simulated interaction of carbon dynamics and nitrogen trace gas fluxes 

using the DAYCENT . P. 303-332. In M.J. Shaffer, Liwang Ma, and S. Hansen (eds.) 

Modeling carbon and nitrogen dynamics for soil management. Lewis Publishers. 

Washington, D.C.

7 Mummey, Daniel L., Jeffrey L. Smith, and George Blum. 1998. Assessment of alternative 

soil management practices on N2O emissions from U.S. agriculture. Agriculture, 

Ecosystems, and Environment 70:79-87.

8 KY. 2001. 200-2001 lime and fertilizer recommendations. 

www.ca.uky.edu/agc/pubs/agr/agr1/agr1.htm

9 Mudahar, Mohinder S. and Travis P. Hignett. 1987. Energy requirements, technology, and 

resources in the fertilizer sector. p. 25-61. In Z.R. Helsel (Ed.) Energy in World Agriculture. 

Vol. 2. Elsevier. New York.
Continuous Corn in the U.S. Corn Belt

20-24

10 Achorn, Frank P., David G. Salladay, and Jeffery L. Greenhill. 1980. Effects of high costs 

on fertilizer production. P. 5-12. Situation 80, TVA Fertilizer Conference. Kansas City, MO.

11 EPA. 2000. Inventory of U.S. greenhouse gas emissions and sinks: 1990-1998. EPA 236-R001. p. 5-17 to 5-19.
21-1

21

WHEAT/FALLOW IN THE WESTERN GREAT PLAINS

21.1 Introduction

As indicated in Chapter 18, wheat/fallow systems represent a large land area in the western Great 

Plains, but the amount of additional carbon sequestered per hectare via reducing tillage is much 

lower than for con/soybean rotations or continuous corn in the Corn Belt. On the other hand, 

reducing tillage on wheat/fallow rotations either reduces N2O emissions or has no significant 

effect on N2O emissions and a smaller adoption incentive is required to get farmers to switch 

from intensive-till to no-till. The net effect is that CO2 abatement costs per tonne CE LC avoided 

are not greatly different than for corn/soybean rotations or continuous corn. The base cases 

(Section 21.2) assume typical levels of carbon sequestration and a small decrease in N2O 

emissions due to reducing tillage and the sensitivity cases (Section 21.3) assume typical levels of 

carbon sequestration and no change in N2O emissions due to reducing tillage.

21.2 Wheat/Fallow (Base Cases)

A summary of effects of switching from intensive-till to no-till on SOC, GHG emissions, and 

costs of avoided GHG emissions is presented in Table 21-1 for wheat/fallow systems in the 

western Great Plains. The bases for the results in Table 21-1 are discussed in the following 

sections using output from the wheat/fallow spreadsheet model developed for this project. Base 

cases assuming a reduction in N2O emissions due to reducing tillage are presented in the first two 

columns of Table 21-1.

21.2.1 Increases in Soil Organic Carbon

Using the same SOC model for wheat/fallow rotations in Nebraska1 as for corn/soybean rotations 

and continuous corn in Iowa2 and Indiana3, switching from intensive-till to no-till was predicted 

to increase SOC an average of 240 kg C/ha/year in Nebraska for the first 10 years after switching 

from intensive-till to no-till. A Nebraska state average was only provided for the first ten years 

after switching to no-till but county averages were provided for 10 and 20 years after switching 

to no-till. For example, the average SOC increase in Kimball County on the western edge of 

Nebraska was estimated at 140 kg C/ha/year for the first 20 years after switching to no-till. 

Another approach for estimating regional increases in SOC due to switching from intensive-till 

to no-till is to use coefficients formulated by the IPCC4. These coefficients are for the first 

20 years after switching from intensive-till to no-till. Using IPPC coefficients for the Mountain 

region which is representative of the drier western portion of the Great Plains with 

predominantly wheat/fallow rotations, switching from intensive-till to no-till resulted in an 
Wheat/Fallow in the Western Great Plains

21-2

estimated weighted-mean increase in SOC of 200 kg C/ha/year. In contrast, a summary of 

wheat/fallow studies in the Great Plains indicated that switching from intensive-till to no-till 

resulted in negligible or no increases in SOC5. A model simulation6 indicated that switching 

from intensive-till to no-till on wheat/fallow systems in eastern Colorado stabilized SOC levels 

that otherwise were declining over time with intensive-till. Because of the variation in regional 

estimates of effects of reducing tillage on SOC levels, results are presented in Table 21-1 

assuming increases of both 200 and 100 kg C/ha/year for a 20-year period due to switching from 

intensive-till to no-till in wheat/fallow systems; an increase of 200 kg C/ha/year was chosen as 

the base case for cost comparisons with other CO2 sink enhancement or storage options.

Table 21-1

Effects of switching from intensive-till to no-till on SOC, GHG emissions, and costs of lifecycle (LC) avoided GHG emissions—Wheat/Fallow in the western great plains

Linear SOC response curve Base-case Δ N2O Zero Δ N2O

 kg C/ha/yr 200 100 200 100

 Years to new steady state (Ys) 20 20 20 20

GHG emissions from tillage-system inputs and N2O (years 1-100), kg CE/ha/y:

 Δ Fuel use -10.3 -10.3 -10.3 -10.3

 Δ Machinery maintenance, trans., and repair -1.9 -1.9 -1.9 -1.9

 Δ N fertilizer use 0 0 0 0

 Δ Herbicide use 11.7 11.7 11.7 11.7

 Δ Total GHG emissions from inputs -0.5 -0.5 -0.5 -0.5

 Δ N2O emissions from soil -25.3 -25.3 0 0

 Δ Total GHG emissions -25.8 -25.8 -0.5 -0.5

Average LC GHG avoided (kg CE/ha/y)

 Years 1 to Ys 226 126 201 101

 Years Ys to 100 26 26 -1 -1

Total LC GHG avoided (years 1-100), 

tonne CE/ha 6.6 4.6 4.1 2.1

NPV Total LC GHG avoided (years 1-100), 

tonne CE/ha 2.7 1.6 2.3 1.2

Costs, $/ha/y

 Transaction (year 1) 5 5 5 5

 Adoption incentive (years 1-20) 15 15 15 15

 Monitoring (years 1-100) 1 1 1 1

Total costs (years 1-100), $/ha 405 405 405 405

NPV Total costs (years 1-100), $/ha 195 195 195 195

Cost (NPV basis), $/tonne CE LC GHG 

avoided 71 123 84 167
Wheat/Fallow in the Western Great Plains

21-3

Example spreadsheet inputs for calculating base-case increases in SOC due to switching from 

intensive-till to no-till in wheat/fallow systems in the western Great Plains (first column of 

Table 21-1) are presented in Table 21-2.

Table 21-2

Example process and economic input variables for switching from intensive-till to no-till 

for wheat/fallow rotation

Process

Alternative Tillage System:

Calculation Method for Sequestering Soil Organic Carbon (SOC)

Quadratic Plateau

Linear Plateau

SOC Sequestered until Steady State 200 kg C/ha/yr

Years until New Steady State 20 years

No-till time period 100 years

SOC Release Rate after no-till time period 70 kg C/ha/yr

Economic

Cost

$/ha/yr

Transaction Costs ( years 1- 1 ) 5.00

Farmer Adoption Incentive to Reduce Tillage ( years 1- 20 ) 15.00

Monitoring Costs ( years 1- 100 ) 1.00

Discount rate 6.09%

Cropland: Two-Year Wheat / Fallow Rotation

Process and Economic Input Variables

(Changes Due to Switching from Intensive-Till to No-Till)

Western Great Plains

No-till

 

21.2.2 Costs

A theoretical basis for estimating conservation-tillage adoption incentives is overviewed in 

Appendix E and an adoption incentive of $15/ha/year is estimated for switching from intensivetill to no-till in wheat/fallow rotations in the western Great Plains. Transaction costs are based 

on experience with forestry projects and are estimated at $5/ha. Monitoring costs are assumed to 

be less than for forestry ($5/ha), assuming that cropland monitoring will be based mainly on 

monitoring practices that have been correlated with changes in SOC rather than direct 

measurements of SOC in each field. Monitoring costs of $1/ha/year were assumed.
Wheat/Fallow in the Western Great Plains

21-4

21.2.3 GHG Effects in Addition to Carbon Storage in Soil Organic Matter

21.2.3.1 Fuel Use

Reductions in fuel use and associated carbon emissions by switching from intensive-till to no-till 

in a wheat/fallow rotation are presented in Table 21-3. Fuel usage rates are from Langemeier 

and Taylor7.

Table 21-3

Reductions in fuel use and associated carbon emissions by switching from intensive-till to 

no-till in a wheat/fallow rotation

gal./acre L/ha gal./acre L/ha kg C/ha

Intensive-till: Intensive-till:

Plant 0.50 4.68 Sweep Plow - 4 2.40 22.44 19.1

Spray - 1 0.10 0.94 Rod Weeder - 2 0.60 5.61 4.8

Total 0.60 5.61 Total 3.00 28.05 23.8

No-till: No-till:

Plant 0.50 4.68 Spray - 3 0.30 2.81 2.4

Spray - 2 0.20 1.87 Total 0.30 2.81 2.4

Total 0.70 6.55

Tillage-system difference Tillage-system difference -21.4

Mean annual tillage-system difference ( years 1- 100 ) -10.3 kg C/ha

Annual fuel saving from reducing tillage -10.3 kg C/ha

Annual machinery MTR savings 18% of fuel savings

Annual machinery MTR saving from reducing tillage ( years 1- 100 ) -1.9 kg C/ha

0.8

Annual Fuel Use

Western Great Plains

Transportation, & Repair (MTR)

Energy Savings Embodied in Machinery Manufacturing, 

4.8

4.0

1.6

0.8

Cropland: Two-Year Wheat / Fallow Rotation

Fuel Use for tillage, planting, and herbicide operations

5.6

Annual Fuel Use

Wheat Portion Fallow Portion

kg C/ha

4.0

21.2.3.2 Machinery Manufacture, Transportation, and Repair

Reductions in machinery manufacture, transportation, and repair (MTR) and associated carbon 

emissions by switching from intensive-till to no-till wheat/fallow rotations were calculated as 

18 percent of the fuel savings (Tables 21-3). The 18 percent factor is based on ratios of tillagesystem fuel use and machinery MTR in a national analysis of tillage-system effects on GHG 

emissions8.
Wheat/Fallow in the Western Great Plains

21-5

21.2.3.3 N Fertilizer Use

In drier climates such as the western Great Plains, reducing tillage has less potential for affecting 

the amount of N fertilizer required. Switching from intensive-till to no-till in wheat/fallow 

systems in the western Great Plains is assumed to have no effect on the amount of N fertilizer 

required.

21.2.3.4 Herbicide Use

In wheat/fallow systems, a switch from intensive-till to no-till involves substituting contact 

herbicide for tillage operations during the fallow period. A herbicide rate of 3.0 kg active 

ingredient/ha during the fallow period is assumed9. This translates to an average of 1.5 kg active 

ingredient/ha/year averaged over the entire wheat/fallow rotation. An estimate of 7.80 kg C 

emitted/kg contact herbicide was used (personal communication, T.O. West, Oak Ridge National 

Laboratory). Example spreadsheet output is presented in Table 21-4.

Table 21-4

Example spreadsheet output for estimating herbicide carbon equivalent emissions for 

wheat/fallow rotation

Mean annual

herbicide rate

kg a.i./ha kg C/kg herbicide (a.i.) kg C/ha/yr

Intensive-till wheat/fallow rotation 0.00

No-till wheat/fallow rotation 1.50

Difference ( years 1- 100 ) 1.50 7.80 11.7

Cropland: Two-Year Wheat / Fallow Rotation

Annual Herbicide Use X Tillage System

Western Great Plains

21.2.3.5 N2O Emissions from Soil

Background on N2O emissions from soil was provided earlier, indicating that N2O emissions are 

dependent on both soil conditions and amount of N fertilizer applied. In this western Great 

Plains wheat/fallow case, the amount of N fertilizer applied does not change with tillage system, 

but soil conditions do change as discussed Chapter 18. Del Grosso et al.6 modeled N2O 

emissions as affected by switching wheat/fallow rotations from intensive-till to no-till on a well 

drained soil in eastern Colorado. The same N fertilizer rate was assumed for both tillage 

systems. A specific placement (e.g., knifing or broadcast) of the N fertilizer was not indicated. 

With these assumptions, N2O emissions were slightly lower with no-till than intensive-till. 

Mummey et al.10 modeled N2O emissions for intensive-till and no-till wheat systems at 467 sites. 

These sites represented the range of soil and environmental conditions for wheat in the United 

States and wheat/fallow systems represent a significant portion of the wheat producing area. 

This modeling effort estimated mean N2O-N emissions of 4.8 kg/ha/year for intensive-till and 
Wheat/Fallow in the Western Great Plains

21-6

4.6 kg/ha/year for no-till. Taking the difference (-0.2 kg N2O-N/ha/year) times 126.4 kg N2O 

CE/kg N2O-N gives -25.3 kg N2O CE/ha/year. This estimate was used for the base cases in 

Table 21-1. Example spreadsheet output is presented in Table 21-5.

Table 21-5

Example spreadsheet output for carbon equivalent emissions from changes in N2O 

emissions for wheat/fallow rotation when changing from intensive-till to no-till

Annual

kg N2O-N/ha kg N2O CE/kg N2O-N kg N2O CE/ha/yr

Intensive-till wheat/fallow rotation 4.80

No-till wheat/fallow rotation 4.60

Difference ( years 1- 100 ) -0.20 126.4 -25.3

Mean annual

N rate IPCC emission factor

kg N/ha kg N2O CE /kg N applied kg N2O CE/ha/yr

Intensive-till wheat/fallow rotation 35.0

No-till wheat/fallow rotation 35.0

Difference ( years 1- 100 ) 0.0 2.65 0.0

Total Difference ( years 1- 100 ) -25.3

Associated with Changes in N Application Rate

Due to Tillage-Related Changes in Soil Properties & Micro-Climate

Cropland: Two-Year Wheat / Fallow Rotation

Annual Changes in N2O Emissions

Western Great Plains

21.2.3.6 Summary of Avoided GHG Emissions

Example spreadsheet output summarizing changes in GHG emissions is presented in Table 21-6. 

This output corresponds to the first column in Table 21-1.
Wheat/Fallow in the Western Great Plains

21-7

Table 21-6

Example spreadsheet output summarizing changes in GHG emissions for wheat/fallow 

rotation when changing from intensive-till to no-till

Changes in GHG Emissions

kg CE/ha/yr

Average Soil Organic Carbon (SOC) sequestered ( years 1 - 20 ) -200

CO2 Emissions from Tillage-System Inputs: ( years 1 - 100 )

Fuel -10.3

Machinery MTR -1.9

N fertilizer 0.0

Herbicides 11.7

Total -0.5

N2O Emissions from Soil ( years 1 - 100 ) -25.3

Avoided GHG Emissions

kg CE/ha/yr tonne CE/ha/yr

( years 1 - 20 ) 225.8 0.226

( years 21 - 100 ) 25.8 0.026

Annual Average

Cropland: Two-Year Wheat / Fallow Rotation

Life Cycle (LC) Avoided GHG Emissions

(Due to Switching from Intensive-Till to No-Till)

Western Great Plains

21.2.4 Summary

Example spreadsheet output summarizing costs and life-cycle (LC) avoided GHG on a carbon 

equivalent (CE) basis is presented in Table 21-7. This output corresponds to the first column in 

Table 21-1.
Wheat/Fallow in the Western Great Plains

21-8

Table 21-7

Example spreadsheet output summarizing costs and life-cycle (LC) avoided GHG on a 

carbon equivalent (CE) basis for wheat/fallow rotation when changing from intensive-till to 

no-till

Process Description

Calculation Method for Sequestering SOC - Linear Plateau No-till time period 100 years

Average SOC Sequestered 200 kg C/ha/yr Release Rate after No-till 0.07 tonne CE/ha/yr

Time until Steady State 20 years

Economic

Cost

($/ha/yr) ($/tonne LC avoided CE)

Average Annual Costs ( years 1 - 1 ) 21.00 93.02

( years 2 - 20 ) 16.00 70.87

( years 21 - 100 ) 1.00 38.82

($/ha) ($/tonne LC avoided CE)

CO2 Sink Enhancement, NPV Basis ( years 1 - 100 ) 191.89 71.09

Cropland: Two-Year Wheat / Fallow Rotation

 Summary

Western Great Plains

LC Avoided CE

(tonne/ha/yr)

0.226

0.226

(tonne/ha)

2.70

0.026

0

1

2

3

4

5

6

7

0 20 40 60 80 100

Years

Avoided Emissions (tonneCE/ha) 

SOC Non SOC Total Avoided GHG Discounted Total Avoided GHG

0

50

100

150

200

250

300

350

400

450

0 20 40 60 80 100

Years

Cost ($/ha) 

Undiscounted Cost Discounted Cost
Wheat/Fallow in the Western Great Plains

21-9

21.2.5 Sensitivity Summary

Example spreadsheet sensitivity summary output is presented in Table 21-8. This sensitivity 

output presents costs assuming adoption incentive payments for 5, 10, 15, and 20 years. These 

costs are presented assuming that no-till is maintained for the full 100-year planning horizon or 

for only as long as adoption incentives are paid to the farmer, after which the land is converted 

back to intensive-till. This analysis assumes that when no-till is switched back to intensive-till, 

SOC levels decrease at a rate of 70 kg/ha/yr. A base case of incentive payments for 10 years was 

selected for comparing costs with other CO2 sink enhancement or storage options.
Wheat/Fallow in the Western Great Plains

21-10

Table 21-8

Wheat/fallow rotation sensitivity summary

Current Input Variable Values

Calculation Method for Sequestering CO2 Linear Plateau

SOC Sequestered until Steady State (kg C/ha/yr) 200

Time Until Steady State (years) 20

SOC Release Rate after no-till time period (tonne C/ha/yr) 0.07

Farmer Adoption Incentive to Reduce Tillage ($/ha/yr) $15.00

Transaction Costs $5.00

Transaction Time Period (years) 1

Monitoring Costs $1.00

Monitoring Time Period (years) 100

After-Tax Discount Rate 6.09%

Sensitivity Input

Farmer Adoption Incentive Time Period Viewed 5 years

10 years

15 years

20 years

Push the adjacent button to update the sensitivity summary costs below.

CO 2 Sink Enhancement Costs (100 year summation)

No-till Time Farmer Adoption Incentive NPV Basis

Period Time Period LC avoided basis

(years) (years) ($/tonne C equivalent)

5 5 182.45

10 10 115.36

15 15 95.15

20 20 86.04

100 5 31.17

100 10 48.54

100 15 61.47

100 20 71.09

Cropland: Two-Year Wheat / Fallow Rotation

Western Great Plains

Sensitivity Summary

Update

Values
Wheat/Fallow in the Western Great Plains

21-11

21.3 Wheat/Fallow (Sensitivity Cases with No Tillage Effect on N2O 

Emissions)

These cases are the same as the western Great Plains wheat/fallow base cases in Section 21.2 

except that no change in N2O emissions is assumed as a result of reducing tillage. These cases 

correspond to columns 3 and 4 of Table 21-1. Assuming no decrease in N2O emissions due to 

switching from intensive-till to no-till results in somewhat higher costs of CO2 abatement ($84 

vs. $71/tonne CE LC avoided in the 200 kg SOC/ha/year case and $167 vs. $123/tonne CE LC 

avoided in the 100 kg SOC/ha/year case). The bases for the results in columns 3 and 4 in 

Table 21-1 are discussed in the following sections using output from the wheat/fallow 

spreadsheet model developed for this project.

21.3.1 Summary of Avoided GHG Emissions

Example spreadsheet output summarizing changes in GHG emissions due to switching from 

intensive-till to no-till wheat/fallow systems with no change in N2O emissions is presented in 

Table 21-9. These results correspond to the third column in Table 21-1.

Table 21-9

Wheat/fallow example spreadsheet output summarizing changes in GHG emissions due to 

switching from intensive-till to no-till with no change in N2O emissions

Changes in GHG Emissions

kg CE/ha/yr

Average Soil Organic Carbon (SOC) sequestered ( years 1 - 20 ) -200

CO2 Emissions from Tillage-System Inputs: ( years 1 - 100 )

Fuel -10.3

Machinery MTR -1.9

N fertilizer 0.0

Herbicides 11.7

Total -0.5

N2O Emissions from Soil ( years 1 - 100 ) 0.0

Avoided GHG Emissions

kg CE/ha/yr tonne CE/ha/yr

( years 1 - 20 ) 200.5 0.200

( years 21 - 100 ) 0.5 0.000

Annual Average

Cropland: Two-Year Wheat / Fallow Rotation

Life Cycle (LC) Avoided GHG Emissions

(Due to Switching from Intensive-Till to No-Till)

Western Great Plains
Wheat/Fallow in the Western Great Plains

21-12

21.3.2 Summary

Example spreadsheet output summarizing costs and life-cycle (LC) avoided GHG on a carbon 

equivalent (CE) basis is presented in Table 21-10. This output corresponds to the third column 

in Table 21-1.

21.3.3 Sensitivity Summary

Example spreadsheet sensitivity summary output is presented in Table 21-11. This output 

corresponds to the third column in Table 21-1 and presents costs assuming adoption incentive 

payments for 5, 10, 15, and 20 years. These costs are presented assuming that no-till is 

maintained for the full 100-year planning horizon or for only as long as adoption incentives are 

paid to the farmer, after which the land is converted back to intensive-till. This analysis assumes 

that when no-till is switched back to intensive-till, SOC levels decrease at a rate of 70 kg/ha/yr. 

A base case of incentive payments for 10 years was selected for comparing costs with other CO2

sink enhancement or storage options.
Wheat/Fallow in the Western Great Plains

21-13

Table 21-10

Wheat/fallow costs and life-cycle (LC) avoided GHG on a carbon equivalent (CE) basis 

when changing from intensive-till to no-till with no change in N2O emissions

Process Description

Calculation Method for Sequestering SOC - Linear Plateau No-till time period 100 years

Average SOC Sequestered 200 kg C/ha/yr Release Rate after No-till 0.07 tonne CE/ha/yr

Time until Steady State 20 years

Economic

Cost

($/ha/yr) ($/tonne LC avoided CE)

Average Annual Costs ( years 1 - 1 ) 21.00 104.75

( years 2 - 20 ) 16.00 79.81

( years 21 - 100 ) 1.00 2095.90

($/ha) ($/tonne LC avoided CE)

CO2 Sink Enhancement, NPV Basis ( years 1 - 100 ) 191.89 83.97

(tonne/ha)

2.29

0.000

(tonne/ha/yr)

0.200

0.200

Cropland: Two-Year Wheat / Fallow Rotation

 Summary

Western Great Plains

LC Avoided CE

0

1

1

2

2

3

3

4

4

5

0 20 40 60 80 100

Years

Avoided Emissions (tonneCE/ha) 

SOC Non SOC Total Avoided GHG Discounted Total Avoided GHG

0

50

100

150

200

250

300

350

400

450

0 20 40 60 80 100

Years

Cost ($/ha) 

Undiscounted Cost Discounted Cost
Wheat/Fallow in the Western Great Plains

21-14

Table 21-11

Wheat/fallow example spreadsheet sensitivity summary output when changing from 

intensive-till to no-till with no change in N2O emissions

Current Input Variable Values

Calculation Method for Sequestering CO2 Linear Plateau

SOC Sequestered until Steady State (kg C/ha/yr) 200

Time Until Steady State (years) 20

SOC Release Rate after no-till time period (tonne C/ha/yr) 0.07

Farmer Adoption Incentive to Reduce Tillage ($/ha/yr) $15.00

Transaction Costs $5.00

Transaction Time Period (years) 1

Monitoring Costs $1.00

Monitoring Time Period (years) 100

After-Tax Discount Rate 6.09%

Sensitivity Input

Farmer Adoption Incentive Time Period Viewed 5 years

10 years

15 years

20 years

Push the adjacent button to update the sensitivity summary costs below.

CO 2 Sink Enhancement Costs (100 year summation)

No-till Time Farmer Adoption Incentive NPV Basis

Period Time Period LC avoided basis

(years) (years) ($/tonne C equivalent)

5 5 182.45

10 10 115.36

15 15 95.15

20 20 86.04

100 5 31.17

100 10 48.54

100 15 61.47

100 20 71.09

Cropland: Two-Year Wheat / Fallow Rotation

Western Great Plains

Sensitivity Summary

Update

Values
Wheat/Fallow in the Western Great Plains

21-15

21.4 References

1 Brenner, J.K., K. Paustian, G. Bluhm, J. Cipra, M.Easter, R. Foulk, K. Killian, R. Moore, J. 

Schuler, P. Smith, and S. Williams. 2002. Quantifying the change in greenhouse gas 

emissions due to natural resource conservation practice application in Nebraska. Colorado 

State University Natural Resource Ecology Laboratory and USDA Natural Resources 

Conservation Service, Fort Collins, CO, USA.

2 Brenner, J., K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, T. Kautza, K. Killian, J. 

Schuler and S. Williams. 2001. Quantifying the change in greenhouse gas emissions due to 

natural resource conservation practice application in Iowa. Final report to the Iowa 

Conservation Partnership. Colorado State University Natural Resource Ecology Laboratory 

and USDA Natural Resources Conservation Service, Fort Collins, CO, USA.

3 Smith, P., J. Brenner, K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, K. Killian, D. 

Lamm, J. Schuler and S. Williams. 2002. Quantifying the change in greenhouse gas 

emissions due to natural resource conservation practice application in Indiana. Final report 

to the Indiana Conservation Partnership. Colorado State University Natural Resource 

Ecology Laboratory and USDA Natural Resources Conservation Service, Fort Collins, CO, 

USA.

4 Eve, M.D., M. Sperow, K. Howerton, K. Paustian, and R.F. Follett. 2002. Predicted impact 

of management changes on soil carbon storage for each cropland region of the conterminous 

United States. J. Soil and Water Conservation. 57 (4) 196-204. 

5 West, Tristram O. and Wlfred M. Post. 2002. Soil organic carbon sequestration rates for 

crops with reduced tillage and enhanced rotation. Submitted to Soil Science Society of 

America Journal.

6 Del Grosso, S.J. W.J. Parton, A.R. Mosier, M.D. Hartman, J. Brenner, D.S. Ojima, and D.S. 

Schimel. 2001. Simulated interaction of carbon dynamics and nitrogen trace gas fluxes 

using the DAYCENT . P. 303-332. In M.J. Shaffer, Liwang Ma, and S. Hansen (eds.) 

Modeling carbon and nitrogen dynamics for soil management. Lewis Publishers. 

Washington, D.C.

7 Langemeier, L.N. and R.K. Taylor. 1998. A look at machinery cost. Kansas Farm 

Management Guide, MF-842. Kansas State University, Department of Agricultural 

Economics.

8 West, Tristram O. and Gregg Marland. 2002. A synthesis of carbon sequestration, carbon 

emissions, and net carbon flux in agriculture: comparing tillage practices in the United 

States. Agriculture, Ecosystems, and Environment (in press)
Wheat/Fallow in the Western Great Plains

21-16

9 Peterson, G.A., D.G Westfall, N.E. Toman, and R.L. Anderson. 1993. Sustainable dryland 

cropping systems: economic analysis. Technical BulletinTB93-3. Colorado State 

University.

10 Mummey, Daniel L., Jeffrey L. Smith, and George Blum. 1998. Assessment of alternative 

soil management practices on N2O emissions from U.S. agriculture. Agriculture, 

Ecosystems, and Environment 70:79-87.
22-1

22

COTTON IN THE SOUTHEASTERN UNITED STATES

22.1 Introduction

Cotton is grown primarily in the southeastern United States. Switching from intensive-till to notill cotton results in somewhat greater increases in SOC than in wheat/fallow systems, primarily 

because with cotton there is no fallow period and cotton residues are added to the soil each year. 

However, primarily because of the warmer more humid climate in the southeast, switching from 

intensive-till to no-till cotton results in significant increases in N2O emissions in contrast to the 

slight decreases in N2O emissions for wheat/fallow. A larger adoption incentive is required to 

get farmers to switch from intensive-till to no-till cotton than is the case for wheat/fallow. The 

net effect of the above factors is that CO2 abatement costs per tonne CE LC avoided are 

significantly greater for cotton than for wheat/fallow. The base cases (Section 22.2) assume 

typical levels of carbon sequestration and a large increase in N2O emissions due to reducing 

tillage and the sensitivity cases (Section 22.3) assume typical levels of carbon sequestration and 

no change in N2O emissions due to reducing tillage.

22.2 Cotton (Base Cases)

A summary of effects of switching from intensive-till to no-till on SOC, GHG emissions, and 

costs of avoided GHG emissions is presented in Table 22-1 for cotton in the southeast. The 

bases for the results in Table 22-1 are discussed in the following sections using output from the 

cotton spreadsheet model developed for this project. Base cases assuming an increase in N2O 

emissions due to reducing tillage are presented in the first two columns of Table 22-1.

22.2.1 Increases in Soil Organic Carbon

A summary of cotton studies in the southeastern U.S. indicated that switching from intensive-till 

to no-till resulted in an average increase in SOC of about 300 kg/ha/year for the first 10 to 

15 years after switching from intensive-till to no-till1. Using IPPC coefficients for the 

southeastern U.S., switching from intensive-till to no-till resulted in an estimated weighted-mean 

increase in SOC of 200 kg C/ha/year for the first 20 years after switching from intensive-till to 

no-till2. Cotton is a significant component of the cropping systems in the southeastern U.S. 

Using IPPC coefficients for the Mississippi Delta region of the United States which also includes 

a significant land area in cotton, switching from intensive-till to no-till resulted in an estimated 

weighted-mean increase in SOC of 520 kg C/ha/year for the first 20 years after switching from 

intensive-till to no-till2. Because of the variation in regional estimates of effects of reducing 

tillage on SOC levels, results are presented in Table 22-1 assuming increases of both 300 and 
Cotton in the Southeastern United States

22-2

150 kg C/ha/year for a 20-year period due to switching from intensive-till to no-till in cotton 

systems; an increase of 300 kg C/ha/year was chosen as the base case for cost comparisons with 

other CO2 sink enhancement or storage options.

Table 22-1

Effects of switching from intensive-till to no-till on SOC, GHG emissions, and costs of lifecycle (LC) avoided GHG emissions—Cotton in the southeastern United States

Linear SOC response curve Base-case Δ N2O Zero Δ N2O

 kg C/ha/yr 300 150 300 150

 Years to new steady state (Ys) 20 20 20 20

GHG emissions from tillage-system inputs and N2O (years 1-100), kg CE/ha/y:

 Δ Fuel use -23.8 -23.8 -23.8 -23.8

 Δ Machinery maint., trans., and repair -4.3 -4.3 -4.3 -4.3

 Δ N fertilizer use 0.0 0.0 0.0 0.0

 Δ Herbicide use 0.0 0.0 0.0 0.0

 Δ Total GHG emissions from inputs -28.1 -28.1 -28.1 -28.1

 Δ N2O emissions from soil 63.2 63.2 0 0

 Δ Total GHG emissions 35.1 35.1 -28.1 -28.1

Average LC GHG avoided (kg CE/ha/y)

 Years 1 to Ys 265 115 328 178

 Years Ys to 100 -35.1 -35.1 28.1 28.1

Total LC GHG avoided (years 1-100), 

tonne CE/ha 1.0 -1.3 7.3 5.1

NPV Total LC GHG avoided (years 1-100),

tonne CE/ha 2.3 0.9 3.4 1.9

Costs, $/ha/y

 Transaction (year 1) 5 5 5 5

 Adoption incentive (years 1-20) 25 25 25 15

 Monitoring (years 1-100) 1 1 1 1

Total costs (years 1-100), $/ha 605 605 605 605

NPV Total costs (years 1-100), $/ha 306 306 306 306

Cost (NPV basis), $/tonne CE LC GHG 

avoided 132 350 91 160
Cotton in the Southeastern United States

22-3

Example spreadsheet inputs for calculating base-case increases in SOC due to switching from 

intensive-till to no-till in cotton in the southeastern U.S. (first column of Table 22-1) are 

presented in Table 22-2.

Table 22-2

Example process and economic input variables for switching from intensive-till to no-till 

for cotton 

Process

Alternative Tillage System:

Calculation Method for Sequestering Soil Organic Carbon (SOC)

Quadratic Plateau

Linear Plateau

SOC Sequestered until Steady State 300 kg C/ha/yr

Years until New Steady State 15 years

No-till time period 100 years

SOC Release Rate after no-till time period 100 kg C/ha/yr

Economic

Cost

$/ha/yr

Transaction Costs ( years 1 - 1 ) 5.00

Farmer Adoption Incentive to Reduce Tillage ( years 1 - 20 ) 25.00

Monitoring Costs ( years 1 - 100 ) 1.00

Discount rate 6.09%

Cropland: Cotton

Process and Economic Input Variables

(Changes Due to Switching from Intensive-Till to No-Till)

Southeastern United States

No-till

22.2.2 Costs

A theoretical basis for estimating conservation-tillage adoption incentives is overviewed in 

Appendix E and an adoption incentive of $25/ha/year is estimated for switching from intensivetill to no-till cotton in the southeastern U.S. Transaction costs are based on experience with 

forestry projects and are estimated at $5/ha. Monitoring costs are assumed to be less than for 

forestry ($5/ha), assuming that cropland monitoring will be based mainly on monitoring 

practices that have been correlated with changes in SOC rather than direct measurements of SOC 

in each field. Monitoring costs of $1/ha/year were assumed.
Cotton in the Southeastern United States

22-4

22.2.3 GHG Effects in Addition to Carbon Storage in Soil Organic Matter

22.2.3.1 Fuel Use

Reductions in fuel use and associated carbon emissions by switching from intensive-till to no-till 

in cotton are presented in Table 22-3. Fuel usage rates are from Langemeier and Taylor3.

Table 22-3

Reductions in fuel use and associated carbon emissions by switching from intensive-till to 

no-till for cotton

gal./acre kg C/ha

Intensive-till:

Chisel plow 0.60 4.8

Disk 0.55 4.4

Cultimulcher 0.60 4.8

Sprayer - 7 0.70 5.6

Plant 0.50 4.0

Cultivate - 3 1.35 10.7

Total 4.30 34.1

No-till:

Chop stalks 0.75

Burndown 0.10 0.8

Plant 0.50 4.0

Sprayer - 7 0.70 5.6

Total 2.05 10.3

Tillage-system difference ( years 1- 100 ) -23.8 kg C/ha

Annual fuel saving from reducing tillage -23.8 kg C/ha

Annual machinery MTR savings 18% of fuel savings

Annual machinery MTR saving from reducing tillage ( years 1- 100 ) -4.3 kg C/ha

Southeastern United States

Cropland: Cotton

Fuel Use for tillage, planting, and herbicide operations

Energy Savings Embodied in Machinery Manufacturing, 

5.14

5.61

6.55

4.68

Transportation, & Repair (MTR)

5.61

4.68

Annual Fuel Use

6.55

12.16

L/ha

12.62

40.21

0.94
Cotton in the Southeastern United States

22-5

22.2.3.2 Machinery Manufacture, Transportation, and Repair

Reductions in machinery manufacture, transportation, and repair (MTR) and associated carbon 

emissions by switching from intensive-till to no-till cotton were calculated as 18 percent of the 

fuel savings (Tables 22-3). The 18 percent factor is based on ratios of tillage-system fuel use and 

machinery MTR in a national analysis of tillage-system effects on GHG emissions4.

22.2.3.3 N Fertilizer Use

N rate recommendations for cotton generally are not adjusted for tillage system in the 

southeastern U.S. Switching from intensive-till to no-till cotton is assumed to have no effect on 

the amount of N fertilizer required.

22.2.3.4 Herbicide Use

With the advent of herbicide resistant cotton, herbicide use doesn’t differ significantly with 

intensive-till and no-till cotton. Intensive-till and no-till cotton were assumed to involve the 

same herbicide usage as presented in Table 22-4.

Table 22-4

Example spreadsheet output for estimating herbicide carbon equivalent emissions for 

cotton

Mean annual

herbicide rate

kg a.i./ha kg C/kg herbicide (a.i.) kg C/ha/yr

Intensive-till cotton 2.71

No-till cotton 2.71

Difference ( years 1- 100 ) 0.00 4.70 0.0

Cropland: Cotton

Annual Herbicide Use X Tillage System

Southeastern United States

22.2.3.5 N2O Emissions from Soil

Background on N2O emissions from soil was provided earlier, indicating that N2O emissions are 

dependent on both soil conditions and amount of N fertilizer applied. In cotton, the amount of 

N fertilizer applied does not change with tillage system, but soil conditions do change as 

discussed Chapter 18. The relatively warm, humid climate in the southeastern U.S. is relatively 

conducive to N2O emissions. Mummey et al.5 modeled N2O emissions for intensive-till and notill cotton at 160 sites. These sites represented the range of soil and environmental conditions for 

cotton in the United States. This modeling effort estimated mean N2O-N emissions of 
Cotton in the Southeastern United States

22-6

6.5 kg/ha/year for intensive-till and 7.0 kg/ha/year for no-till. Taking the difference (0.5 kg 

N2O-N/ha/year) times 126.4 kg N2O CE/kg N2O-N gives 63.2 kg N2O CE/ha/year. This estimate 

was used for the base cases in Table 22-1. Example spreadsheet output is presented in Table 

22-5.

Table 22-5

Example spreadsheet output for carbon equivalent emissions from changes in N2O 

emissions for cotton when changing from intensive-till to no-till

Annual

kg N2O-N/ha kg N2O CE/kg N2O-N kg N2O CE/ha/yr

Intensive-till cotton 6.50

No-till cotton 7.00

Difference ( years 1- 100 ) 0.50 126.4 63.2

Mean annual

N rate IPCC emission factor

kg N/ha kg N2O CE /kg N applied kg N2O CE/ha/yr

Intensive-till cotton 60.0

No-till cotton 60.0

Difference ( years 1- 100 ) 0.0 2.65 0.0

Total Difference ( years 1- 100 ) 63.2

Associated with Changes in N Application Rate

Due to Tillage-Related Changes in Soil Properties & Micro-Climate

Cropland: Cotton

Annual Changes in N2O Emissions

Southeastern United States

22.2.3.6 Summary of Avoided GHG Emissions

Example spreadsheet output summarizing changes in GHG emissions is presented in Table 22-6. 

This output corresponds to the first column in Table 22-1.
Cotton in the Southeastern United States

22-7

Table 22-6

Example spreadsheet output summarizing changes in GHG emissions for cotton when 

changing from intensive-till to no-till

Changes in GHG Emissions

kg CE/ha/yr

Average Soil Organic Carbon (SOC) sequestered ( years 1 - 15 ) -300

CO2 Emissions from Tillage-System Inputs: ( years 1 - 100 )

Fuel -23.8

Machinery MTR -4.3

N fertilizer 0.0

Herbicides 0.0

Total -28.1

N2O Emissions from Soil ( years 1 - 100 ) 63.2

Avoided GHG Emissions

kg CE/ha/yr tonne CE/ha/yr

( years 1 - 15 ) 264.9 0.265

( years 16 - 100 ) -35.1 -0.035

Annual Average

Cropland: Cotton

Life Cycle (LC) Avoided GHG Emissions

(Due to Switching from Intensive-Till to No-Till)

Southeastern United States

22.2.4 Summary

Example spreadsheet output summarizing costs and life-cycle (LC) avoided GHG on a carbon 

equivalent (CE) basis is presented in Table 22-7. This output corresponds to the first column in 

Table 22-1.
Cotton in the Southeastern United States

22-8

Table 22-7

Example spreadsheet output summarizing costs and life-cycle (LC) avoided GHG on a 

carbon equivalent (CE) basis for cotton when changing from intensive-till to no-till

Process Description

Calculation Method for Sequestering SOC - Linear Plateau No-till time period 100 years

Average SOC Sequestered 300 kg C/ha/yr Release Rate after No-till 0.1 tonne CE/ha/yr

Time until Steady State 15 years

Economic

Cost

$/ha/yr $/tonne LC avoided CE

Average Annual Costs ( years 1 - 1 ) 31.00 117.02

( years 2 - 15 ) 26.00 98.15

( years 16 - 20 ) 26.00 -740.76

( years 21 - 100 ) 1.00 -28.49

($/ha) ($/tonne LC avoided CE)

CO2 Sink Enhancement, NPV Basis ( years 1 - 100 ) 305.75 131.69

Cropland: Cotton

 Summary

Southeastern United States

LC Avoided CE

tonne/ha/yr

0.265

0.265

-0.035

2.322

(tonne/ha)

-0.035

-4

-3

-2

-1

0

1

2

3

4

5

0 10 20 30 40 50 60 70 80 90 100

Years

Avoided Emissions (tonne CE/ha) 

SOC Non SOC Total Avoided GHG Discounted Total Avoided GHG

0

100

200

300

400

500

600

700

0 10 20 30 40 50 60 70 80 90 100

Years

Cost ($/ha) 

Undiscounted Cost Discounted Cost
Cotton in the Southeastern United States

22-9

22.2.5 Sensitivity Summary

Example spreadsheet sensitivity summary output is presented in Table 22-8. This sensitivity 

output presents costs assuming adoption incentive payments for 5, 10, 15, and 20 years. These 

costs are presented assuming that no-till is maintained for the full 100-year planning horizon or 

for only as long as adoption incentives are paid to the farmer, after which the land is converted 

back to intensive-till. This analysis assumes that when no-till cotton is switched back to 

intensive-till, SOC levels decrease at a rate of 70 kg/ha/yr. A base case of incentive payments 

for 10 years was selected for comparing costs with other CO2 sink enhancement or storage 

options.
Cotton in the Southeastern United States

22-10

Table 22-8

Cotton rotation sensitivity summary

Current Input Variable Values

Calculation Method for Sequestering CO2 Linear Plateau

SOC Sequestered until Steady State (kg C/ha/yr) 300

Time Until Steady State (years) 15

SOC Release Rate after no-till time period (tonne C/ha/yr) 0.1

Farmer Adoption Incentive to Reduce Tillage ($/ha/yr) $25.00

Transaction Costs $5.00

Transaction Time Period (years) 1

Monitoring Costs $1.00

Monitoring Time Period (years) 100

After-Tax Discount Rate 6.09%

Sensitivity Input

Farmer Adoption Incentive Time Period Viewed 5 years

10 years

15 years

20 years

Push the adjacent button to update the sensitivity summary costs below.

CO 2 Sink Enhancement Costs ( years 1-100 )

No-till Time Farmer Adoption Incentive NPV Basis

Period Time Period LC avoided GHG basis

(years) (years) ($/tonne C equivalent)

5 5 234.84

10 10 172.19

15 15 135.16

20 20 149.97

100 5 54.33

100 10 87.99

100 15 113.05

100 20 131.69

Cropland: Cotton

Sensitivity Summary

Southeastern United States
Cotton in the Southeastern United States

22-11

22.3 Cotton (Sensitivity Cases with No Tillage Effect on N2O Emissions)

These cases are the same as the cotton base cases in Section 22.2 except that no change in N2O 

emissions is assumed as a result of reducing tillage. These cases correspond to columns 3 and 4 

of Table 22-1. Assuming no increase in N2O emissions due to switching from intensive-till to 

no-till results in significantly lower costs of CO2 abatement ($132 vs. $91/tonne CE LC avoided 

in the 300 kg SOC/ha/year case and $350 vs. $160/tonne CE LC avoided in the 150 kg 

SOC/ha/year case). The bases for the results in columns 3 and 4 in Table 22-1 are discussed in 

the following sections using output from the cotton spreadsheet model developed for this project.

22.3.1 Summary of Avoided GHG Emissions

Example spreadsheet output summarizing changes in GHG emissions due to switching from 

intensive-till to no-till cotton with no change in N2O emissions is presented in Table 22-9. These 

results correspond to the third column in Table 22-1.

Table 22-9

Cotton example spreadsheet output summarizing changes in GHG emissions due to 

switching from intensive-till to no-till with no change in N2O emissions

Changes in GHG Emissions

kg CE/ha/yr

Average Soil Organic Carbon (SOC) sequestered ( years 1 - 15 ) -300

CO2 Emissions from Tillage-System Inputs: ( years 1 - 100 )

Fuel -23.8

Machinery MTR -4.3

N fertilizer 0.0

Herbicides 0.0

Total -28.1

N2O Emissions from Soil ( years 1 - 100 ) 0.0

Avoided GHG Emissions

kg CE/ha/yr tonne CE/ha/yr

( years 1 - 15 ) 328.1 0.328

( years 16 - 100 ) 28.1 0.028

Annual Average

Cropland: Cotton

Life Cycle (LC) Avoided GHG Emissions

(Due to Switching from Intensive-Till to No-Till)

Southeastern United States
Cotton in the Southeastern United States

22-12

22.3.2 Summary

Example spreadsheet output summarizing costs and life-cycle (LC) avoided GHG on a carbon 

equivalent (CE) basis is presented in Table 22-10. This output corresponds to the third column 

in Table 22-1.

22.3.3 Sensitivity Summary

Example spreadsheet sensitivity summary output is presented in Table 22-11. This sensitivity 

output corresponds to the third column in Table 22-1 and presents costs assuming adoption 

incentive payments for 5, 10, 15, and 20 years. These costs are presented assuming that no-till is 

maintained for the full 100-year planning horizon or for only as long as adoption incentives are 

paid to the farmer, after which the land is converted back to intensive-till. This analysis assumes 

that when no-till is switched back to intensive-till, SOC levels decrease at a rate of 70 kg/ha/yr. 

A base case of incentive payments for 10 years was selected for comparing costs with other CO2

sink enhancement or storage options.
Cotton in the Southeastern United States

22-13

Table 22-10

Cotton costs and life-cycle (LC) avoided GHG on a carbon equivalent (CE) basis when 

changing from intensive-till to no-till with no change in N2O emissions

Process Description

Calculation Method for Sequestering SOC - Linear Plateau No-till time period 100 years

Average SOC Sequestered 300 kg C/ha/yr Release Rate after No-till 0.1 tonne CE/ha/yr

Time until Steady State 15 years

Economic

Cost

$/ha/yr $/tonne LC avoided CE

Average Annual Costs ( years 1 - 1 ) 31.00 94.48

( years 2 - 15 ) 26.00 79.24

( years 16 - 20 ) 26.00 925.23

( years 21 - 100 ) 1.00 35.59

($/ha) ($/tonne LC avoided CE)

CO2 Sink Enhancement, NPV Basis ( years 1 - 100 ) 305.75 91.08

Cropland: Cotton

 Summary

Southeastern United States

LC Avoided CE

tonne/ha/yr

0.328

0.328

0.028

3.357

(tonne/ha)

0.028

0

1

2

3

4

5

6

7

8

0 10 20 30 40 50 60 70 80 90 100

Years

Avoided Emissions (tonne CE/ha) 

SOC Non SOC Total Avoided GHG Discounted Total Avoided GHG

0

100

200

300

400

500

600

700

0 10 20 30 40 50 60 70 80 90 100

Years

Cost ($/ha) 

Undiscounted Cost Discounted Cost
Cotton in the Southeastern United States

22-14

Table 22-11

Cotton example spreadsheet sensitivity summary output when changing from intensive-till 

to no-till with no change in N2O emissions

Current Input Variable Values

Calculation Method for Sequestering CO2 Linear Plateau

SOC Sequestered until Steady State (kg C/ha/yr) 300

Time Until Steady State (years) 15

SOC Release Rate after no-till time period (tonne C/ha/yr) 0.1

Farmer Adoption Incentive to Reduce Tillage ($/ha/yr) $25.00

Transaction Costs $5.00

Transaction Time Period (years) 1

Monitoring Costs $1.00

Monitoring Time Period (years) 100

After-Tax Discount Rate 6.09%

Sensitivity Input

Farmer Adoption Incentive Time Period Viewed 5 years

10 years

15 years

20 years

Push the adjacent button to update the sensitivity summary costs below.

CO 2 Sink Enhancement Costs ( years 1-100 )

No-till Time Farmer Adoption Incentive NPV Basis

Period Time Period LC avoided GHG basis

(years) (years) ($/tonne C equivalent)

5 5 157.14

10 10 123.84

15 15 102.84

20 20 110.85

100 5 37.58

100 10 60.86

100 15 78.19

100 20 91.08

Cropland: Cotton

Sensitivity Summary

Southeastern United States
Cotton in the Southeastern United States

22-15

22.4 References

1 West, Tristram O. and Wlfred M. Post. 2002. Soil organic carbon sequestration rates for 

crops with reduced tillage and enhanced rotation. Submitted to Soil Science Society of 

America Journal.

2 Eve, M.D., M. Sperow, K. Howerton, K. Paustian, and R.F. Follett. 2002. Predicted impact 

of management changes on soil carbon storage for each cropland region of the conterminous 

United States. J. Soil and Water Conservation. 57 (4) 196-204.

3 Langemeier, L.N. and R.K. Taylor. 1998. A look at machinery cost. Kansas Farm 

Management Guide, MF-842. Kansas State University, Department of Agricultural 

Economics.

4 West, Tristram O. and Gregg Marland. 2002. A synthesis of carbon sequestration, carbon 

emissions, and net carbon flux in agriculture: comparing tillage practices in the United 

States. Agriculture, Ecosystems, and Environment (in press).

5 Mummey, Daniel L., Jeffrey L. Smith, and George Blum. 1998. Assessment of alternative 

soil management practices on N2O emissions from U.S. agriculture. Agriculture, 

Ecosystems, and Environment 70:79-87.

23-1

23

SUMMARY COMPARISONS AND CONCLUSIONS

Presented in this chapter are comparisons among options evaluated in this project and between 

CO2 capture, transport and storage and indirect capture by forests and cropland via reducing 

tillage using the costs of capture from another EPRI study1.

23.1 Transport and Storage

Shown in Table 23-1 is a comparison of the transport and storage costs of captured CO2

developed in this study on a $/tonne of CO2 and $/tonne of C on a levelized annual equivalent 

life cycle greenhouse gas avoided basis rounded off to the nearest dollar. Bear in mind that the 

transport costs include a small portion of the compression costs normally included in the capture 

costs in many studies. This was necessitated by the use of the capture costs from the previous 

EPRI study which only compressed the CO2 to 83 bar (1200 psia) rather than the 153 bar (2200 

psia) required at the pipeline inlet. This difference represents a small fraction of the costs of CO2

transport and storage ($0.75/tonne of CO2 or $2.7/tonne of C). Included are the base case and a 

range for each cost. These cases are intended to represent realistic ranges of the important 

variables and the details of the cases can be found in the appropriate chapter for each storage 

method. Plotted in Figure 23-1 are the values on a CO2 basis to visually represent the 

comparisons.

Table 23-1

CO2 transport and storage cost comparison on a levelized annual CO2 equivalent life cycle 

greenhouse gas avoided basis

Case

Base Case High Cost 

Case

Low Cost 

Case

Base 

Case

High 

Cost 

Case

Low 

Cost 

Case

$/tonne 

CO2

$/tonne 

CO2

$/tonne 

CO2

$/tonne 

C

$/tonne 

C

$/tonne 

C

EOR (12) 74 (91) (45) 271 (334)

ECBMR (6) 19 (26) (20) 69 (94)

Depleted Gas 

Res.

5 19 1 18 71 4 

Depleted Oil Res. 4 11 1 14 41 4 

Aquifer 3 12 1 11 43 4 

Ocean Pipeline 6 14 3 20 52 11 

Ocean Tanker 18 23 16 65 84 58 
Summary Comparisons and Conclusions

23-2

(12) (6)

5 4 3 6

18

(100)

(80)

(60)

(40)

(20)

0

20

40

60

80

100

EOR ECBMR Depleted Gas Depleted Oil Aquifer Ocean Pipeline Ocean Tanker

$/t CO2 Eq. LC GHG Avoided

Figure 23-1

CO2 transport and storage cost comparison on a levelized annual $/t of CO2 equivalent life 

cycle greenhouse gas avoided basis

In looking at the comparisons, the base case for EOR and ECBMR show a negative value base 

case and larger ranges than the other cases. This illustrates that, at the oil price used and no 

separate charge for CO2, these base cases operate at a profit. The large ranges show that the 

costs are very sensitive to changes in the variables. This illustrates how site specific these cases 

may be. The depleted gas, oil, aquifer and ocean pipeline are similar in value. Their ranges are 

more compressed although they still change by several factors. The tanker case evaluates as the 

most expensive and has the smallest range. This case is driven by the capital costs of the tanker, 

port, and platforms and the variables chosen in the high and low case only impact a small portion 

of the costs.

23.2 Capture, Transport and Storage

As discussed in Chapter 2, in order to compare capture, transport and storage with the indirect 

sequestration options, all of the cases have been put on a net present value (NPV) basis. This 

occurs because of the wide range of emissions and costs as a function of time over the 100-year 

span of the evaluation. Shown in Table 23.2 are the cases developed in this study on a $/tonne of 

CO2 and $/tonne of C on a NPV equivalent life cycle greenhouse gas avoided basis rounded off 

to the nearest dollar. A plot of the $/t CO2 information is shown in Figure 23.2.
Summary Comparisons and Conclusions

23-3

Table 23-2

CO2 capture, transport and storage cost comparison on a NPV equivalent life cycle 

greenhouse gas avoided basis

Case

Base Case Base Case

$/tonne 

CO2

$/tonne C

EOR 4 15 

ECBMR 11 41 

Depleted Gas Res. 23 86 

Depleted Oil Res. 22 81 

Aquifer 21 77 

Ocean Pipeline 24 89 

Ocean Tanker 39 143 

0

5

10

15

20

25

30

35

40

45

EOR ECBMR Gas Reservoir Oil Reservoir Aquifer Ocean

Pipeline

Ocean Tanker

$/t CO2 Eq. LC GHG Avoided

Figure 23-2

CO2 capture, transport and storage cost comparison on a NPV $/t of CO2 equivalent life 

cycle greenhouse gas avoided basis
Summary Comparisons and Conclusions

23-4

23.3 Forest management 

Shown in Table 23-3 is a comparison of the forestry cases developed in this study on a $/tonne of 

CO2 and $/tonne of C on a NPV life cycle greenhouse gas avoided basis. Sensitivity to 

productivity are indicated where data was available to calculate the case. The information in this 

table is for forestry cases that include above and below ground carbon and timber products & 

revenues.

Table 23-3

Forestry cases NPV cost comparison (LC equivalent greenhouse gas avoided, including 

above and below ground carbon and timber products & revenues).

Case

Base Case -

Medium

Productivity

Low Cost 

Case-High

Productivity

High Cost 

Case-Low

Productivity

Base Case -

Medium

Productivity

Low Cost 

Case-High

Productivity

High Cost 

Case-Low

Productivity

$/tonne CO2 $/tonne CO2 $/tonne CO2 $/tonne C $/tonne C $/tonne C

US Pine 15 11 54 40 

US Fir 2 1 7 3 

Cedar (15) (14) (11) (56) (51) (39)

Pine-Oak 1 (1) 2 2 (3) 6 

Miombo (24) (87)

Mango (43) (158)

Deforest 3 10 

Where data was available, productivity (carbon stored/area), could affect costs significantly. 

Shown in Figure 23-3 is a bar chart of the $/tonne CO2 costs from Table 26.2 to visually 

represent the comparisons.
Summary Comparisons and Conclusions

23-5

-50

-40

-30

-20

-10

0

10

20

US Pine USFir Cedar Pine-Oak Miombo Mango Deforest

$/tonne CO2 Eq. LC GHG Avoided

Above + Below ground + products + revenues

Figure 23-3

Forestry cost comparison on a NPV $/t of CO2 equivalent life cycle greenhouse gas 

avoided basis

The only case with a significant cost is the US pine case and this is because the land costs are 

high and the timber revenues are not enough to offset them. US fir and Pine-Oak are generally 

revenue neutral and the Cedar, Miombo and Mango are negative costs due to the high value of 

their products. Prevention of deforestation is also a low-cost option.

23.4 Cropland via Reducing Tillage 

Shown in Table 23-4 are the cases developed in this study for the cropland cases on a $/tonne of 

CO2 and $/tonne of C on a NPV equivalent life cycle greenhouse gas avoided basis rounded off 

to the nearest dollar. A plot of the $/t CO2 information is shown in Figure 23-4.
Summary Comparisons and Conclusions

23-6

Table 23-4

Cropland cases NPV equivalent life cycle greenhouse gas avoided basis

Case

Base 

Case-10 y 

Incentive

High Cost 

Case- 20 y 

Incentive

Low Cost 

Case-5 y 

Incentive

Base 

Case-10 y 

incentive

High Cost 

Case- 20 y 

Incentive

Low Cost 

Case-5 y 

Incentive

$/tonne 

CO2

$/tonne 

CO2

$/tonne 

CO2

$/tonne C $/tonne C $/tonne C

Corn/soybean 13 20 8 48 72 30 

Continuous 

corn

14 21 8 51 77 30 

Wheat/fallow 13 19 9 49 71 32 

Continuous 

cotton

24 36 15 88 132 54 

0

5

10

15

20

25

30

Wheat/fallow Corn/soybean Continuous corn Continuous cotton

$/t CO2 Eq. LC GHG Avoided

Figure 23-4

Cropland cases NPV $/t CO2 equivalent life cycle greenhouse gas avoided basis

All of these options are fairly expensive compared to the other options in the study. However, 

the length of time that the incentive has to be paid has a significant impact on the cost.
Summary Comparisons and Conclusions

23-7

23.5 Direct Verses Indirect Sequestration

Shown in Figure 23-5 is a comparison of all of the base case values in $/tonne of CO2 equivalent 

LC GHG avoided on a net present value plotted from lowest to highest cost.

-50

-40

-30

-20

-10

0

10

20

30

40

50

Mango

Miombo

Cedar

Pine-Oak

USFir

Deforest

EOR

ECBMR

Wheat/fallow

Corn/soybean

Continuous corn

US Pine

Aquifer

Oil Reservoir

Gas Reservoir

Ocean Pipeline

Continuous cotton

Ocean Tanker

Base Case

$/tonne of CO2 Eq. LC GHG Avoided

Figure 23-5

Comparison of direct and indirect sequestration cases.

As shown in Figure 23-5, all of the forestry options are the least expensive except for the US 

Pine case. Next are the EOR and ECBMR options followed by most of the cropland options. 

Figure 26-7 includes the available high and low cases. The main impact of these cases is on the 

cropland cases where the low cases would make them competitive with ECBMR and the high 

cases would make them about the same as the other geologic storage cases.
Summary Comparisons and Conclusions

23-8

-50

-40

-30

-20

-10

0

10

20

30

40

50

Mango

Miombo

Cedar

Pine-Oak

USFir

Deforest

EOR

ECBMR

Wheat/falow

Corn/soybean

Continuous corn

US Pine

Aquifer

Oil Reservoir

Gas Reservoir

Ocean Pipeline

Continuous cotton

Ocean Tanker

$/t CO2 Eq. LC GHG Avoided

Base Case

High Case

Low Case

Figure 23-6

Comparison of direct and indirect sequestration cases including high and low cases.

Base-case forest and cropland areas required to offset LC GHG emissions avoided by base-case 

CO2 capture and storage options are presented in Table 23-5. These land area equivalents are for 

a 100-year planning horizon. The carbon equivalent (CE) LC GHG emissions avoided by the 

capture and storage options assume CO2 capture and injection into storage reservoirs during the 

20-year book life of the capture and storage plants and storage of injected CO2 for another 

80 years. For the ocean storage options, the CE LC GHG avoided is reduced for leakage of CO2

throughout the 100-year storage period. Zero CO2 leakage is assumed for the other CO2 storage 

options. The CE LC GHG emissions avoided by the forest and cropland options assume that the 

improved forestry or cropland practices are maintained for 100 years. Generally, ten times or 

more cropland than forest land is required to offset the CO2 storage options.
Summary Comparisons and Conclusions

23-9

Table 23-5

Base-case forest and cropland areas required to offset LC GHG emissions avoided by base -case CO2 capture and storage 

options.

Aquifer Storage Enhanced Oil Recovery Depleted Oil Reservoirs Depleted Gas Reservoirs Coal Bed Methane Recovery Ocean Pipeline Storage Ocean Tanker Storage

Forestry / Cropland Forestry / Cropland Forestry / Cropland Forestry / Cropland Forestry / Cropland Forestry / Cropland Forestry / Cropland

Forestry System - Medium Productivity Area to Equal Area to Equal Area to Equal Area to Equal Area to Equal Area to Equal Area to Equal 

CO 2 Capture & Storage CO2 Capture & Storage CO2 Capture & Storage CO 2 Capture & Storage CO2 Capture & Storage CO2 Capture & Storage CO 2 Capture & Storage

 (tonne CE/ha) (Mtonne CE) (Mha) (Mtonne CE) (Mha) (Mtonne CE) (Mha) (Mtonne CE) (Mha) (Mtonne CE) (Mha) (Mtonne CE) (Mha) (Mtonne CE) (Mha)

Plantation, USA, Loblolly Pine 183.14 9.81 0.05 9.09 0.05 9.81 0.05 9.81 0.05 9.54 0.05 28.28 0.15 28.00 0.15

Plantation, USA, Douglas Fir 452.65 9.81 0.02 9.09 0.02 9.81 0.02 9.81 0.02 9.54 0.02 28.28 0.06 28.00 0.06

Plantation (Taungya), Mexico, Spanish cedar 277.40 9.81 0.04 9.09 0.03 9.81 0.04 9.81 0.04 9.54 0.03 28.28 0.10 28.00 0.10

Restoration, Mexico, Pine-oak 183.39 9.81 0.05 9.09 0.05 9.81 0.05 9.81 0.05 9.54 0.05 28.28 0.15 28.00 0.15

Restoration, Southern Africa, Miombo 59.26 9.81 0.17 9.09 0.15 9.81 0.17 9.81 0.17 9.54 0.16 28.28 0.48 28.00 0.47

Agro- forestry, Southern India, Mango- tamarind 71.53 9.81 0.14 9.09 0.13 9.81 0.14 9.81 0.14 9.54 0.13 28.28 0.40 28.00 0.39

Avoidance of deforestation, Mexico,Various 207.66 9.81 0.05 9.09 0.04 9.81 0.05 9.81 0.05 9.54 0.05 28.28 0.14 28.00 0.13

Cropland System

Two Year Corn/Soybean Rotation 6.51 9.81 1.51 9.09 1.39 9.81 1.51 9.81 1.51 9.54 1.46 28.28 4.34 28.00 4.30

( Intensive-till to No-till )

Continuous Corn 12.33 9.81 0.80 9.09 0.74 9.81 0.80 9.81 0.80 9.54 0.77 28.28 2.29 28.00 2.27

( Intensive-till to No-till )

Continuous Cotton 0.99 9.81 9.90 9.09 9.18 9.81 9.90 9.81 9.90 9.54 9.64 28.28 28.56 28.00 28.28

( Intensive-till to No-till )

Two Year Wheat / Fallow Rotation 6.58 9.81 1.49 9.09 1.38 9.81 1.49 9.81 1.49 9.54 1.45 28.28 4.30 28.00 4.26

( Intensive-till to No-till )

(Above/below ground C, non-CO2 GHGs, & timber 

products/revenues)

Equivalent Forestry / Cropland Area Required to

Offset Geologic & Ocean Options

LC Avoided GHG Basis (100 year summation)
Summary Comparisons and Conclusions

23-10

23.6 Conclusions

Making these comparisons was a challenge due to the differences between direct and indirect 

cases. The resulting numbers should only be used as a relative indication of cost. Site-specific 

characteristics could significantly change the outcome of the comparisons. In this study, the 

forestry options are very attractive along with captur e using EOR for storage. Other factors, 

such as land availability and storage option location and capacity will also play a significant part 

in which options make the most sense at a given site.

The current work should only be considered a starting point. Better cost information and 

improvements to processes are likely to be developed and need to be incorporated into updates of 

this information. In addition, there are plans to update and add to portions of the current work to 

answer questions that arose but were beyond the scope of the current effort. For example, what 

happens to the costs if the power plant operates for longer than the plant book life? Also, how do 

the economics look for other power generation options? These and other questions will be 

evaluated in updates to this work.

23.7 REFERENCES

1 DOE/EPRI. 2000. Evaluation of innovative fossil fuel power plants with CO2 removal. 

EPRI, Palo Alto, California; U.S. Department of Energy–Office of Fossil Energy, 

Germantown, Maryland, and U.S. Department of Energy/NETL, Pittsburgh, Pennsylvania: 

1000316.
A-1

A

DETAILED LIFE-CYCLE GHG EMISSIONS AVOIDED 

VIA CAPTURING AND STORING CO2

Detailed life-cycle (LC) GHG emissions avoided via capturing and storing CO2 in the base cases 

considered in this project are presented in Tables A-1 to A-7. Methodology for calculating these 

emissions is described in Chapters 2 and 3.
Detailed Life-Cycle GHG Emissions Avoided Via Capturing and Storing CO2

A-2

Table A-1

EOR base case detailed analysis of LC GHG emissions avoided

Construction $ of Total Plant TPC

Coal Flow Carbon Carbon Rail Transport Flue Gas Energy Cost per Tonne Power Plant

tonne/day wt. % Emission, % Km tonne/day GJ/tonne of Steel M$

Case 3a 3,474 63.75% 8.68% 100 51,599 28 $50,000 $662,484

Case 3b 3,136 63.75% 99.70% 100 53,383 28 $50,000 $535,983

With CO2 Capture (Case 3a) Without CO2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N20 CH4 CO 2 N 20 CH 4

Coal Mine 33,698 0.5 1.6 Coal Mine 30,417 0.5 1.4

Mine Methane Vented 639 minor 2,901 Mine Methane Vented 577 minor 2,619

Coal Train 1,821 minor minor Coal Train 1,821 minor minor

Power Plant Emissions 205,812 11.0 minor Power Plant Emissions 2,133,828 11.4 minor

Power Plant Construction 1,243 0.0 0.0 Power Plant Construction 1,006 0.0 0.0

Power Plant Decommissioning 124 0.0 0.0 Power Plant Decommissioning 101 0.0 0.0

Total Power Plant 243,338 12 2,903 Total Power Plant 2,167,749 12 2,620

100 Year Multiplier 1 296 23 100 Year Multiplier 1 296 23

Total Power Plant CO2 Equivalent 243,338 3,433 66,762 Total Power Plant CO2 Equivalent 2,167,749 3,530 60,262

Total (mg CO2 Equivalent / kWh) 86,053 1,214 23,609 Total (mg CO 2 Equivalent / kWh) 728,612 1,186 20,255

Power Plant Total ( mg CO 2 Equivalent / kWh) 110,876 Power Plant Total ( mg CO 2 Equivalent / kWh) 750,053

Compression of Captured CO2 to Pipeline Spec

CO2 Compression

Compression Power 2,650 kW 1,598 0.1 19

Construction 6.1 0.0 0.0

Decommissioning 0.6 0.0 0.0

Total 1,605 0.1 19

100 Year Multiplier 1 296 23

CO2 Equivalent 1,605 23 438

CO2 Compression Total (CO 2 Equivalent ) 2,066

Transportation & Sequestration of Captured CO 2

CO2 Transportation

Pipeline Construction 913 0.0 0.0

Pipeline Decommissioning 91 0.0 0.0

Total 1,005 0 0

CO2 Sequestration-EOR

Power 25,142 kW 128,378 1 155

Plant Construction

 Aboveground Equipment 279 0.0 0.0

 Subsurface Equipment 64 0.0 0.0

Plant Decommissioning 34 0.0 0.0

Total 128,755 1 155

CO2 Transportation & Sequestration Total 129,759 1 155

100 Year Multiplier 1 296 23

CO2 Equivalent 129,759 210 3,569

Transportation & Sequestration Total (CO 2 Equivalent ) 133,538

Amount of Emitted & Avoided GHG (CO 2 Equivalents)

With Without 

Capture CO2 Capture CO2 Capture Difference Storage (CO2 Compression, Transportation, and Sequestration)

kg CO2 equivalent emitted/kWh 0.111 0.750 0.639 Compression CO2 equivalent emitted, tonne/yr 2,066

CO2 equivalent avoided, tonne/yr 1,807,446 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 133,538

Total CO2 equivalent emitted, tonne/yr 135,604

Total System

CO2 equivalent avoided by capture, tonne/yr 1,807,446

CO2 equivalent emitted from storage, tonne/yr 135,604

CO2 equivalent LC avoided, tonne/yr 1,671,842

CO2 equivalent LC avoided, % of CO2 captured 77.5%

CO2 equivalent LC avoided, % of CO2 equivalent avoided during capture 92.5%

Enhanced Oil Recovery: Greenhouse Gas Life-Cycle (LC) Analysis

GHG Emissions

tonnes/year

GHG Emissions

tonnes/year

GHG (CO2 Equivalents) Emmited During CO2 Capture & Storage
Detailed Life-Cycle GHG Emissions Avoided Via Capturing and Storing CO2

A-3

Table A-2

ECBMR base case detailed analysis of LC GHG emissions avoided

Construction $ of Total Plant TPC

Coal Flow Carbon Carbon Rail Transport Flue Gas Energy Cost per Tonne Power Plant

tonne/day wt. % Emission, % Km tonne/day GJ/tonne of Steel M$

Case 3a 3,474 63.75% 8.68% 100 51,599 28 $50,000 $662,484

Case 3b 3,136 63.75% 99.70% 100 53,383 28 $50,000 $535,983

With CO2 Capture (Case 3a) Without CO2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N20 CH4 CO 2 N 20 CH 4

Coal Mine 33,698 0.5 1.6 Coal Mine 30,417 0.5 1.4

Mine Methane Vented 639 minor 2,901 Mine Methane Vented 577 minor 2,619

Coal Train 1,821 minor minor Coal Train 1,821 minor minor

Power Plant Emissions 205,812 11.0 minor Power Plant Emissions 2,133,828 11.4 minor

Power Plant Construction 1,243 0.0 0.0 Power Plant Construction 1,006 0.0 0.0

Power Plant Decommissioning 124 0.0 0.0 Power Plant Decommissioning 101 0.0 0.0

Total Power Plant 243,338 12 2,903 Total Power Plant 2,167,749 12 2,620

100 Year Multiplier 1 296 23 100 Year Multiplier 1 296 23

Total Power Plant CO2 Equivalent 243,338 3,433 66,762 Total Power Plant CO2 Equivalent 2,167,749 3,530 60,262

Total (mg CO2 Equivalent / kWh) 86,053 1,214 23,609 Total (mg CO 2 Equivalent / kWh) 728,612 1,186 20,255

Power Plant Total ( mg CO 2 Equivalent / kWh) 110,876 Power Plant Total ( mg CO 2 Equivalent / kWh) 750,053

Compression of Captured CO2 to Pipeline Spec

CO2 Compression

Compression Power 2,650 kW 1,598 0.1 19

Construction 6.1 0.0 0.0

Decommissioning 0.6 0.0 0.0

Total 1,605 0.1 19

100 Year Multiplier 1 296 23

CO2 Equivalent 1,605 23 438

CO2 Compression Total (CO 2 Equivalent ) 2,066

Transportation & Sequestration of Captured CO 2

CO2 Transportation

Pipeline Construction 913 0.0 0.0

Pipeline Decommissioning 91 0.0 0.0

Total 1,005 0 0

CO2 Sequestration-ECBM

Power 9,259 kW 47,276 0.3 57

Plant Construction

 Aboveground Equipment 42 0.0 0.0

 Subsurface Equipment 73 0.0 0.0

Plant Decommissioning 12 0 0

Total 47,402 0 57

CO2 Transportation & Sequestration Total 48,407 0 57

100 Year Multiplier 1 296 23

CO2 Equivalent 48,407 77 1,314

Transportation & Sequestration Total (CO 2 Equivalent ) 49,798

Amount of Emitted & Avoided GHG (CO 2 Equivalents)

With Without Storage (CO2 Compression, Transportation, and Sequestration)

Capture CO2 Capture CO2 Capture Difference Compression CO2 equivalent emitted, tonne/yr 2,066

kg CO2 equivalent emitted/kWh 0.111 0.750 0.639 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 49,798

CO2 equivalent avoided, tonne/yr 1,807,446 Total CO2 equivalent emitted, tonne/yr 51,864

Total System

CO2 equivalent avoided by capture, tonne/yr 1,807,446

CO2 equivalent emitted from storage, tonne/yr 51,864

CO2 equivalent LC avoided, tonne/yr 1,755,582

CO2 equivalent LC avoided, % of CO2 captured 81.4%

CO2 equivalent LC avoided, % of CO2 equivalent avoided during capture 97.1%

GHG (CO2 Equivalents) Emmited During CO2 Capture & Storage

Enhanced Coal Bed Methane: Greenhouse Gas Life-Cycle (LC) Analysis

GHG Emissions

tonnes/year

GHG Emissions

tonnes/year
Detailed Life-Cycle GHG Emissions Avoided Via Capturing and Storing CO2

A-4

Table A-3

Aquifer storage base case detailed analysis of LC GHG emissions avoided

Construction $ of Total Plant TPC

Coal Flow Carbon Carbon Rail Transport Flue Gas Energy Cost per Tonne Power Plant

tonne/day wt. % Emission, % Km tonne/day GJ/tonne of Steel M$

Case 3a 3,474 63.75% 8.68% 100 51,599 28 $50,000 $662,484

Case 3b 3,136 63.75% 99.70% 100 53,383 28 $50,000 $535,983

With CO2 Capture (Case 3a) Without CO2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N20 CH4 CO 2 N 20 CH 4

Coal Mine 33,698 0.5 1.6 Coal Mine 30,417 0.5 1.4

Mine Methane Vented 639 minor 2,901 Mine Methane Vented 577 minor 2,619

Coal Train 1,821 minor minor Coal Train 1,821 minor minor

Power Plant Emissions 205,812 11.0 minor Power Plant Emissions 2,133,828 11.4 minor

Power Plant Construction 1,243 0.0 0.0 Power Plant Construction 1,006 0.0 0.0

Power Plant Decommissioning 124 0.0 0.0 Power Plant Decommissioning 101 0.0 0.0

Total Power Plant 243,338 12 2,903 Total Power Plant 2,167,749 12 2,620

100 Year Multiplier 1 296 23 100 Year Multiplier 1 296 23

Total Power Plant CO2 Equivalent 243,338 3,433 66,762 Total Power Plant CO2 Equivalent 2,167,749 3,530 60,262

Total (mg CO2 Equivalent / kWh) 86,053 1,214 23,609 Total (mg CO 2 Equivalent / kWh) 728,612 1,186 20,255

Power Plant Total ( mg CO 2 Equivalent / kWh) 110,876 Power Plant Total ( mg CO 2 Equivalent / kWh) 750,053

Compression of Captured CO2 to Pipeline Spec

CO2 Compression

Compression Power 2,650 kW 1,598 0.1 19

Construction 6.1 0.0 0.0

Decommissioning 0.6 0.0 0.0

Total 1,605 0.1 19

100 Year Multiplier 1 296 23

CO2 Equivalent 1,605 23 438

CO2 Compression Total (CO 2 Equivalent ) 2,066

Transportation & Sequestration of Captured CO 2

CO2 Transportation

Pipeline Construction 913 0.0 0.0

Pipeline Decommissioning 91 0.0 0.0

Total 1,005 0 0

CO2 Sequestration-Aquifer Storage

Power minor minor minor

Plant Construction

 Aboveground Equipment 3 0 0

 Subsurface Equipment 0 0 0

Plant Decommissioning 0 0 0

Total 4 0 0

CO2 Transportation & Sequestration Total 1,008 0 0

100 Year Multiplier 1 296 23

CO2 Equivalent 1,008 0 0

Transportation & Sequestration Total (CO 2 Equivalent ) 1,008

Amount of Emitted & Avoided CO2 

With Without 

Capture CO2 Capture CO2 Capture Difference Storage (CO2 Compression, Transportation, and Sequestration)

kg CO2 equivalent emitted/kWh 0.111 0.750 0.639 Compression CO2 equivalent emitted, tonne/yr 2,066

CO2 equivalent avoided, tonne/yr 1,807,446 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 1,008

Total CO2 equivalent emitted, tonne/yr 3,074

Total System

CO2 equivalent avoided by capture, tonne/yr 1,807,446

CO2 equivalent emitted from storage, tonne/yr 3,074

CO2 equivalent LC avoided, tonne/yr 1,804,372

CO2 equivalent LC avoided, % of CO2 captured 83.6%

CO2 equivalent LC avoided, % of CO2 equivalent avoided during capture 99.8%

Aquifer Storage: Greenhouse Gas Life-Cycle (LC) Analysis

GHG Emissions

tonnes/year

GHG Emissions

tonnes/year
Detailed Life-Cycle GHG Emissions Avoided Via Capturing and Storing CO2

A-5

Table A-4

Depleted oil reservoir base case detailed analysis of LC GHG emissions avoided

Construction $ of Total Plant TPC

Coal Flow Carbon Carbon Rail Transport Flue Gas Energy Cost per Tonne Power Plant

tonne/day wt. % Emission, % Km tonne/day GJ/tonne of Steel M$

Case 3a 3,474 63.75% 8.68% 100 51,599 28 $50,000 $662,484

Case 3b 3,136 63.75% 99.70% 100 53,383 28 $50,000 $535,983

With CO2 Capture (Case 3a) Without CO2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N20 CH4 CO 2 N 20 CH 4

Coal Mine 33,698 0.5 1.6 Coal Mine 30,417 0.5 1.4

Mine Methane Vented 639 minor 2,901 Mine Methane Vented 577 minor 2,619

Coal Train 1,821 minor minor Coal Train 1,821 minor minor

Power Plant Emissions 205,812 11.0 minor Power Plant Emissions 2,133,828 11.4 minor

Power Plant Construction 1,243 0.0 0.0 Power Plant Construction 1,006 0.0 0.0

Power Plant Decommissioning 124 0.0 0.0 Power Plant Decommissioning 101 0.0 0.0

Total Power Plant 243,338 12 2,903 Total Power Plant 2,167,749 12 2,620

100 Year Multiplier 1 296 23 100 Year Multiplier 1 296 23

Total Power Plant CO2 Equivalent 243,338 3,433 66,762 Total Power Plant CO2 Equivalent 2,167,749 3,530 60,262

Total (mg CO2 Equivalent / kWh) 86,053 1,214 23,609 Total (mg CO 2 Equivalent / kWh) 728,612 1,186 20,255

Power Plant Total ( mg CO 2 Equivalent / kWh) 110,876 Power Plant Total ( mg CO 2 Equivalent / kWh) 750,053

Compression of Captured CO2 to Pipeline Spec

CO2 Compression

Compression Power 2,650 kW 1,598 0.1 19

Construction 6.1 0.0 0.0

Decommissioning 0.6 0.0 0.0

Total 1,605 0.1 19

100 Year Multiplier 1 296 23

CO2 Equivalent 1,605 23 438

CO2 Compression Total (CO 2 Equivalent ) 2,066

Transportation & Sequestration of Captured CO 2

CO2 Transportation

Pipeline Construction 913 0.0 0.0

Pipeline Decommissioning 91 0.0 0.0

Total 1,005 0 0

CO2 Sequestration-Depleted Oil Reservoir

Power minor minor minor

Plant Construction

 Aboveground Equipment 5 0.0 0.0

 Subsurface Equipment 12 0.0 0.0

Plant Decommissioning 2 0.0 0.0

Total 19 0 0

CO2 Transportation & Sequestration Total 1,023 0 0

100 Year Multiplier 1 296 23

CO2 Equivalent 1,023 0 0

Transportation & Sequestration Total (CO 2 Equivalent ) 1,024

Amount of Emitted & Avoided CO2 

With Without 

Capture CO2 Capture CO2 Capture Difference Storage (CO2 Compression, Transportation, and Sequestration)

kg CO2 equivalent emitted/kWh 0.111 0.750 0.639 Compression CO2 equivalent emitted, tonne/yr 2,066

CO2 equivalent avoided, tonne/yr 1,807,446 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 1,024

Total CO2 equivalent emitted, tonne/yr 3,089

Total System

CO2 equivalent avoided by capture, tonne/yr 1,807,446

CO2 equivalent emitted from storage, tonne/yr 3,089

CO2 equivalent LC avoided, tonne/yr 1,804,356

CO2 equivalent LC avoided, % of CO2 captured 83.6%

CO2 equivalent LC avoided, % of CO2 equivalent avoided during capture 99.8%

Depleted Oil Reservoir: Greenhouse Gas Life-Cycle (LC) Analysis

GHG Emissions

tonnes/year

GHG Emissions

tonnes/year
Detailed Life-Cycle GHG Emissions Avoided Via Capturing and Storing CO2

A-6

Table A-5

Depleted gas reservoir base case detailed analysis of LC GHG emissions avoided

Construction $ of Total Plant TPC

Coal Flow Carbon Carbon Rail Transport Flue Gas Energy Cost per Tonne Power Plant

tonne/day wt. % Emission, % Km tonne/day GJ/tonne of Steel M$

Case 3a 3,474 63.75% 8.68% 100 51,599 28 $50,000 $662,484

Case 3b 3,136 63.75% 99.70% 100 53,383 28 $50,000 $535,983

With CO2 Capture (Case 3a) Without CO2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N20 CH4 CO 2 N 20 CH 4

Coal Mine 33,698 0.5 1.6 Coal Mine 30,417 0.5 1.4

Mine Methane Vented 639 minor 2,901 Mine Methane Vented 577 minor 2,619

Coal Train 1,821 minor minor Coal Train 1,821 minor minor

Power Plant Emissions 205,812 11.0 minor Power Plant Emissions 2,133,828 11.4 minor

Power Plant Construction 1,243 0.0 0.0 Power Plant Construction 1,006 0.0 0.0

Power Plant Decommissioning 124 0.0 0.0 Power Plant Decommissioning 101 0.0 0.0

Total Power Plant 243,338 12 2,903 Total Power Plant 2,167,749 12 2,620

100 Year Multiplier 1 296 23 100 Year Multiplier 1 296 23

Total Power Plant CO2 Equivalent 243,338 3,433 66,762 Total Power Plant CO2 Equivalent 2,167,749 3,530 60,262

Total (mg CO2 Equivalent / kWh) 86,053 1,214 23,609 Total (mg CO 2 Equivalent / kWh) 728,612 1,186 20,255

Power Plant Total ( mg CO 2 Equivalent / kWh) 110,876 Power Plant Total ( mg CO 2 Equivalent / kWh) 750,053

Compression of Captured CO2 to Pipeline Spec

CO2 Compression

Compression Power 2,650 kW 1,598 0.1 19

Construction 6.1 0.0 0.0

Decommissioning 0.6 0.0 0.0

Total 1,605 0.1 19

100 Year Multiplier 1 296 23

CO2 Equivalent 1,605 23 438

CO2 Compression Total (CO 2 Equivalent ) 2,066

Transportation & Sequestration of Captured CO 2

CO2 Transportation

Pipeline Construction 913 0.0 0.0

Pipeline Decommissioning 91 0.0 0.0

Total 1,005 0 0

CO2 Sequestration-Depleted Gas Reservoir

Power minor minor minor

Plant Construction

 Aboveground Equipment 6 0.0 0.0

 Subsurface Equipment 27 0.0 0.0

Plant Decommissioning 3 0.0 0.0

Total 36 0 0

CO2 Transportation & Sequestration Total 1,041 0 0

100 Year Multiplier 1 296 23

CO2 Equivalent 1,041 0 0

Transportation & Sequestration Total (CO 2 Equivalent ) 1,041

Amount of Emitted & Avoided CO2 

With Without 

Capture CO2 Capture CO2 Capture Difference Storage (CO2 Compression, Transportation, and Sequestration)

kg CO2 equivalent emitted/kWh 0.111 0.750 0.639 Compression CO2 equivalent emitted, tonne/yr 2,066

CO2 equivalent avoided, tonne/yr 1,807,446 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 1,041

Total CO2 equivalent emitted, tonne/yr 3,107

Total System

CO2 equivalent avoided by capture, tonne/yr 1,807,446

CO2 equivalent emitted from storage, tonne/yr 3,107

CO2 equivalent LC avoided, tonne/yr 1,804,339

CO2 equivalent LC avoided, % of CO2 captured 83.6%

CO2 equivalent LC avoided, % of CO2 equivalent avoided during capture 99.8%

Depleted Gas Reservoir: Greenhouse Gas Life-Cycle (LC) Analysis

GHG Emissions

tonnes/year

GHG Emissions

tonnes/year
Detailed Life-Cycle GHG Emissions Avoided Via Capturing and Storing CO2

A-7

Table A-6

Ocean pipeline storage base case detailed analysis of LC GHG emissions avoided

Construction $ of Total Plant TPC

Coal Flow Carbon Carbon Rail Transport Flue Gas Energy Cost per Tonne Power Plant

tonne/day wt. % Emission, % Km tonne/day GJ/tonne of Steel M$

Case 3a 3,474 63.75% 8.68% 100 51,599 28 $50,000 $662,484

Case 3b 3,136 63.75% 99.70% 100 53,383 28 $50,000 $535,983

With CO2 Capture (Case 3a) Without CO2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO2 N 20 CH 4 CO 2 N 20 CH 4

Coal Mine 33,698 0.5 1.6 Coal Mine 30,417 0.5 1.4

Mine Methane Vented 639 minor 2,901 Mine Methane Vented 577 minor 2,619

Coal Train 1,821 minor minor Coal Train 1,821 minor minor

Power Plant Emissions 205,812 11.0 minor Power Plant Emissions 2,133,828 11.4 minor

Power Plant Construction 1,243 0.0 0.0 Power Plant Construction 1,006 0.0 0.0

Power Plant Decommissioning 124 0.0 0.0 Power Plant Decommissioning 101 0.0 0.0

Total Power Plant 243,338 12 2,903 Total Power Plant 2,167,749 12 2,620

100 Year Multiplier 1 296 23 100 Year Multiplier 1 296 23

Total Power Plant CO2 Equivalent 243,338 3,433 66,762 Total Power Plant CO2 Equivalent 2,167,749 3,530 60,262

Total (mg CO2 Equivalent / kWh) 86,053 1,214 23,609 Total (mg CO2 Equivalent / kWh) 728,612 1,186 20,255

Power Plant Total ( mg CO 2 Equivalent / kWh) 110,876 Power Plant Total (mg CO 2 Equivalent / kWh ) 750,053

Compression of Captured CO2 to Pipeline Spec

CO2 Compression

Compression Power 7,950 kW 4,794 0.2 57

Construction 18.3 0.0 0.0

Decommissioning 1.8 0.0 0.0

Total 4,814 0.2 57

100 Year Multiplier 1 296 23

CO2 Equivalent 4,814 68 1,315

CO2 Compression Total (CO 2 Equivalent ) 6,197

Transportation & Sequestration of Captured CO 2

CO2 Transportation

Pipeline Construction 913 0.0 0.0

Pipeline Decommissioning 91 0.0 0.0

Total 1,005 0 0

CO2 Sequestration-Ocean

Compression Power 5,650 kW 3,407 0.2 41

Construction 17.6 0.0 0.3

Decommissioning 2 0.0 0.0

Ocean Pipeline Construction 1155 0.0 0.0

Ocean Pipeline Decommissioning 116 0.0 0.0

Total 4,698 0.2 41

CO2 Transportation & Sequestration Total 5,702 0.2 41

100 Year Multiplier 1 296 23

CO2 Equivalent 5,702 48 941

Transportation & Sequestration Total (CO 2 Equivalent ) 6,692

Amount of Emitted & Avoided GHG (CO2 Equivalents)

With Without 

Capture CO2 Capture CO2 Capture Difference

kg CO2 equivalent emitted/kWh 0.111 0.750 0.639 Storage (CO2 Compression, Transportation, and Sequestration)

CO2 equivalent avoided for a single IGCC plant, tonne/yr 1,807,446 Compression CO2 equivalent emitted, tonne/yr 6,197

Number of IGCC plants transporting CO 2 to ocean site 3 Transportation and sequestration CO2 equivalent emitted, tonne/yr 6,692

Total CO2 equivalent avoided, tonne/yr 5,422,337 Total CO2 equivalent emitted, tonne/yr 12,889

Total System

CO2 equivalent avoided by capture, tonne/yr 5,422,337

CO2 equivalent emitted from storage, tonne/yr 12,889

CO2 equivalent LC avoided w/o leakage, tonne/yr 5,409,447

CO2 equivalent LC avoided w/o leakage, % of CO 2 captured 83.6%

CO2 equivalent LC avoided w/o leakage, % of CO 2 equivalent avoided during capture 99.8%

GHG Emissions GHG Emissions

tonnes/year tonnes/year

GHG (CO2 Equivalents) Emmited During CO2 Capture & Storage

Ocean Pipeline Storage: Greenhouse Gas Life-Cycle (LC) Analysis (without leakage)
Detailed Life-Cycle GHG Emissions Avoided Via Capturing and Storing CO2

A-8

Table A-7

Ocean tanker storage base case detailed analysis of LC GHG emissions avoided

Construction $ of Total Plant TPC

Coal Flow Carbon Carbon Rail Transport Flue Gas Energy Cost per Tonne Power Plant

tonne/day wt. % Emission, % Km tonne/day GJ/tonne of Steel M$

Case 3a 3,474 63.75% 8.68% 100 51,599 28 $50,000 $662,484

Case 3b 3,136 63.75% 99.70% 100 53,383 28 $50,000 $535,983

With CO2 Capture (Case 3a) Without CO2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO2 N 20 CH 4 CO 2 N 20 CH 4

Coal Mine 33,698 0.5 1.6 Coal Mine 30,417 0.5 1.4

Mine Methane Vented 639 minor 2,901 Mine Methane Vented 577 minor 2,619

Coal Train 1,821 minor minor Coal Train 1,821 minor minor

Power Plant Emissions 205,812 11.0 minor Power Plant Emissions 2,133,828 11.4 minor

Power Plant Construction 1,243 0.0 0.0 Power Plant Construction 1,006 0.0 0.0

Power Plant Decommissioning 124 0.0 0.0 Power Plant Decommissioning 101 0.0 0.0

Total Power Plant 243,338 12 2,903 Total Power Plant 2,167,749 12 2,620

100 Year Multiplier 1 296 23 100 Year Multiplier 1 296 23

Total Power Plant CO2 Equivalent 243,338 3,433 66,762 Total Power Plant CO2 Equivalent 2,167,749 3,530 60,262

Total (mg CO2 Equivalent / kWh) 86,053 1,214 23,609 Total (mg CO2 Equivalent / kWh) 728,612 1,186 20,255

Power Plant Total ( mg CO 2 Equivalent / kWh) 110,876 Power Plant Total (mg CO 2 Equivalent / kWh ) 750,053

Compression of Captured CO2 to Pipeline Spec

CO2 Compression

Compression Power 7,950 kW 4,794 0.2 57

Construction 18.3 0.0 0.0

Decommissioning 1.8 0.0 0.0

Total 4,814 0.2 57

100 Year Multiplier 1 296 23

CO2 Equivalent 4,814 68 1,315

CO2 Compression Total (CO 2 Equivalent ) 6,197

Transportation & Sequestration of Captured CO 2

CO2 Transportation

Pipeline Construction 913 0.0 0.0

Pipeline Decommissioning 91 0.0 0.0

Total 1,005 0 0

CO2 Sequestration-Ocean

Tanker Emissions from Fuel 2,395 0 0

Tanker Boil Off 53,362 0 0

Construction 1034 0.0 15.3

Decommissioning 103 0.0 1.5

56,895 0 17

CO2 Transportation & Sequestration Total 57,899 0 17

100 Year Multiplier 1 296 23

CO2 Equivalent 57,899 2 386

Transportation & Sequestration Total (CO 2 Equivalent ) 58,287

Amount of Emitted & Avoided GHG (CO2 Equivalents)

With Without 

Capture CO2 Capture CO2 Capture Difference

kg CO2 equivalent emitted/kWh 0.111 0.750 0.639 Storage (CO2 Compression, Transportation, and Sequestration)

CO2 equivalent avoided, tonne/yr 1,807,446 Compression CO2 equivalent emitted, tonne/yr 6,197

Number of IGCC plants transporting CO 2 to ocean site 3 Transportation and sequestration CO2 equivalent emitted, tonne/yr 58,287

Total CO2 equivalent avoided, tonne/yr 5,422,337 Total CO2 equivalent emitted, tonne/yr 64,484

Total System

CO2 equivalent avoided by capture, tonne/yr 5,422,337

CO2 equivalent emitted from storage, tonne/yr 64,484

CO2 equivalent LC avoided, tonne/yr 5,357,852

CO2 equivalent LC avoided, % of CO 2 captured 82.8%

CO2 equivalent LC avoided, % of CO 2 equivalent avoided during capture 98.8%

GHG Emissions GHG Emissions

tonnes/year tonnes/year

GHG (CO2 Equivalents) Emmited During CO2 Capture & Storage

Ocean Tanker Storage: Greenhouse Gas Life-Cycle (LC) Analysis (without leakage)
B-1

B

EXPLANATION OF PROCESS INPUT AND SUMMARY

SPREADSHEETS

Process Input sheet

The Process Input sheet includes key physical and economic parameters for estimating CO2

storage costs. As for the entire spreadsheet, parameters in blue and contained in rectangular 

boxes can be varied by the user.

Design Basis Section

Definitions of these parameters can be found in the ‘Methodology Used’ section of the relevant 

chapter. Base-case estimates of these parameters are entered in the orientation version of the 

spreadsheet. The design basis assumes that one IGCC (Case 3a) CO2 capture plant provides the 

CO2 for the EOR, ECBMR, and geologic storage options. In the case of the ocean pipeline and 

ocean tanker storage options, however, it is assumed that the quantity of CO2 handled is equal to 

that supplied by three IGCC plants.

Economic Section

For the EOR and ECBMR options, the revenue generated from the enhanced production can 

offset or partially offset CO2 storage costs. In the case of the other storage options, however, 

there is no value-added product.

EOR and ECBMR Storage Options Only

The economic section of the Process Input sheet is structured the same for the EOR and ECBMR 

options. Therefore, the description that is given below, which directly relates to the EOR case, 

also applies to the ECBMR scenario.

For EOR, Wellhead Oil Price is the key economic parameter. The Wellhead Oil Price and the 

Oil Production rate (Design Basis section) determine the enhanced oil revenue from the EOR 

operation. The CO2 storage cost minus enhanced oil revenue gives the net cost of storing CO2. 

When oil revenue is greater than the CO2 storage cost, the net cost of storing CO2 is negative 

(i.e., a revenue). From the perspective of an EOR operation, a positive net storage cost equa ls 

the breakeven CO2 tipping fee. A negative net storage cost equals the breakeven CO2 purchase 

price. Note that a Breakeven Oil Price is provided in the Process Input sheet. This is the 
Explanation of Process Input and Summary Spreadsheets

B-2

Wellhead Oil Price required for oil revenues to offset the cost of CO2 storage. Finally, the Oil 

Royalty is the percent of the Wellhead Oil price received by the owner of the oil rights and the 

Wellhead Oil Price minus the Oil Royalty gives the Oil Credit.

All Storage Options

The After-Tax Discount Rate (see Table 2-1 in Section 2.1.1) is used to calculate the net present 

value (NPV) of CO2 revenues (i.e., CO2 price times tonnes CO2 abated) and CO2 abatement costs 

to reflect the time-value of money. For regulated economics, the return on debt and equity is set 

by regulators and the discount rate equals the overall annual return to debt and equity. With 

assumptions in Table 2-1 of Section 2.1.1, the after-tax discount rate is 6.09 percent. Note that 

the carrying charge varies with discount rate because, in a regulated environment, the discount 

rate is the overall annual return to debt and equity, a parameter that is built into the carrying 

charge. A 6.09 percent After-tax Discount Rate gives a 15.0 percent Levelized Carrying Charge 

Factor.

Summary Sheet

The four sections in this sheet summarize the cost of capturing and storing CO2. The bottom-line 

costs in Summary sheet sections 1-3 are in units of $/tonne CO2 captured or avoided. Costs in 

Summary sheet section 4 are in units of $/MWh and indicate incremental effects of CO2 capture, 

CO2 storage, and Year 21-100 storage costs on cost of electricity (COE).

Summary Sheet Section 1—Levelized Annual CO2 Storage Costs (Years 1-20)

Summary sheet section 1 provides results for a levelized annual storage cost analysis for years 1-

20, the book life of the storage operation.

Costs in Summary sheet section 1 are presented on a CO2 Captured Basis (column 1) and a LifeCycle (LC) Avoided GHG Basis for storage only (column 3). General approaches for 

calculating costs on these bases are described on in Sections 2.2 and 2.3.

Summary Sheet Section 2—Levelized Annual CO2 Capture and Net Storage Costs 

(Years 1-20)

This section is similar to Summary sheet section 1, but presents costs for capture and storage 

combined. Costs in this section are presented on a CO2 Captured Basis (column 1), CO2

Avoided Basis for capture and storage combined (column 2) and a Life-Cycle (LC) Avoided 

GHG Basis for capture and storage combined (column 3). General procedures for calculating 

costs on these bases are described in Sections 2.2 and 2.3 of this report.
Explanation of Process Input and Summary Spreadsheets

B-3

Summary Sheet Section 3—CO2 Capture and Storage Costs, NPV Basis 

(Years 1-100)

The structure of this section is the same as for Summary sheet section 2 except that the first four 

lines contain net present values (NPVs) for years 1-100 instead of annual levelized values for 

years 1-20. The last line in Summary sheet section 3 contains costs in units of $/tonne CO2; 

these bottom-line costs are calculated as the breakeven CO2 price required to offset CO2 capture 

and storage costs. Note that this breakeven CO2 price is constant over time. This is the price at 

which the NPV of CO2 revenues (i.e., CO2 price times CO2 stored or avoided) equals the NPV of 

costs for CO2 capture and storage combined. The theory for calculating the breakeven CO2 price 

is presented in Section 2.4. The same theory and procedures are used for the forestry and 

cropland CO2 sink enhancement options in this project. Using these procedures, the bottom-line 

costs ($/tonne CO2 equivalent of GHG avoided on a life-cycle basis) in column 3 of Summary

sheet section 3 can be compared with life-cycle costs for forestry and cropland sink enhancement 

options that differ greatly from CO2 capture/storage options in the timing of when costs occur 

and when CO2 is stored and/or leaks back to the atmosphere.

Equation (8) in Section 2.4 reduces to a ratio of total CO2 abatement costs to total CO2 abatement 

for cases in which annual CO2 storage costs and annual CO2 abatement change proportionally 

over time. For the CO2 capture and storage options in this project, CO2 abatement costs and CO2

abatement are levelized over time for years 1-20. This means that the bottom-line costs in 

Summary sheet section 3 ($/tonne of CO2) are the same as in Summary sheet section 2 unless 

monitoring and/or transaction costs occur in years 21-100 without corresponding levels of CO2

abatement. The difference in bottom-line costs for Summary sheet sections 2 and 3 is also 

increased if CO2 leaks occur in years 21-100.

Summary Sheet Section 4—Cost of Electricity, $/MWh

Costs in Summary sheet section 4 are in units of $/MWh and indicate incremental effects of CO2

capture (years 1-20), CO2 storage (years 1-20), and CO2 storage (years 21-100, e.g., monitoring 

and/or transaction costs) on COE. Procedures for calculating these incremental effects on COE 

are in Section 2.1.4.

C-1

C

STATUS AND COSTS OF EXISTING FORESTRY 

SEQUESTRATION PROJECTS

The first forestry-based greenhouse gas offset projects took place in the early 1990s, as shown in 

Table C-1

1

. These were voluntary projects since there were no legal requirements to abate 

greenhouse gases. The voluntary aspect was somewhat reflected in the price paid for carbon 

sequestration, which averaged about $0.2/t C based upon the costs to the investor. The signing 

of the UN Framework Convention on Climate Change in Rio de Janeiro in 1992 launched the 

concept of Joint Implementation to reduce greenhouse gas emissions. This led to an increase in 

investment in forestry sequestration. Between 1992 and the First Conference of the Parties 

(COP-1) in 1995, the average price paid for carbon sequestration is estimated to be about $2/t C, 

10 times higher than before. At COP-1, developing countries’ concerns about the concept of 

Joint Implementation resulted in a compromise that consisted of a pilot phase during which 

projects were called Activities Implemented Jointly (AIJ). These projects were conducted with 

the objective of establishing protocols and creating experiences but without allowing carbon 

crediting between developed countries. In this new environment where companies were faced 

with great uncertainty about the potential value of projects, there was a large reduction in 

investment in new sequestration projects and the willingness to pay for sequestration fell to 

$0.6/t C. In the year preceding COP-3, in Kyoto in December 1997, there was great anticipation 

that binding commitments would be agreed, which would entail hard costs for industrialized

countries. This led to an increase in the level of investment in forestry sequestration projects and 

the average price paid inc reased to $12/tC. Following COP-3, there was even more interest in 

forestry sequestration and the price paid for carbon credits in some cases rose as high as 

$20-25/t C. The supply of carbon offsets became more organized and more sophisticated. This 

is the case for the Costa Rican national program, which is the first producer-led carbon-offset 

initiative in the world and the first to utilize independent certification and insurance.
Status and Costs of Existing Forestry Sequestration Projects

C-2

Table C-1

Forestry sequestration projects initiated until mid-1998

Project name

Date 

proposed / 

initiated

Carbon 

offset

(1000 t C)

Area

(ha)

Host 

country

Investor 

country

Project 

description

AES—Care 1990 10,500 186,000 Guatemala USA Agroforestry

Face Malaysia 1992 4,250 25,000 Malaysia Netherlands Enrichment 

planting

Face—Kroknose 1992 3,080 16,000 Czech R. Netherlands Park rehabilitation

Face Netherlands 1992 885 5,000 Netherlands Netherlands Urban forestry

ICSB—NEP 1 1992 56 1,400 Malaysia USA Reduced impact 

logging

AES—Oxfam—Coica 1992 15,000 1,500,000 South America USA Forest protection

ASS—Nature 

Conservancy

1992 15,380 58,000 Paraguay USA Forest protection

Face—Profafor 1993 9,660 75,000 Ecuador Netherlands Small farmers 

plantation forestry

RUSAFOR—SAP 1993 79 450 Russia USA Plantation forestry

Face Uganda 1994 6,750 27,000 Uganda Netherlands Forest 

rehabilitation

Rio Bravo 1994 1,300 87,000 Belize USA Forest protection 

and management

Carfix 1994 2,000 91,000 Costa Rica USA Forest protection, 

and management

Ecoland/Tenaska 1995 350 2,500 Costa Rica USA Forest 

conservation

ICSB—NEP 2 1996 39 980 Malaysia USA Reduced Impact 

Logging

Noel Kempif M. 1996 14,000 1,000,000 Bolivia UK/USA Forest 

conservation and 

management

Klinki Forestry 1997 1,600 87,000 Costa Rica USA Reforestation with 

klinki

Burkina Faso 1997 67 300,000 Burkina Faso Denmark Fire wood 

community 

forestry

Scolel Te 1997 15 13,000 Mexico UK/France Community 

forestry
Status and Costs of Existing Forestry Sequestration Projects

C-3

Table C-1 (continued)

Forestry sequestration projects initiated until mid-1998

Project name

Date 

proposed / 

initiated

Carbon 

offset

(1000 t C)

Area

(ha)

Host 

country

Investor 

country

Project 

description

PAP OCIC 1997 18,000 570,000 Costa Rica Norway, USA Forest 

conservation

Norway—Costa Rica 1997 230 4,000 Costa Rica Norway Forest 

rehabilitation and 

conservation

Tesco ‘green petrol’ 1998 n.a. n.a. Undefined UK Forestry

Green fleet initiative 1997 n.a. n.a. Australia Australia Reforestation

AES—Ilha Bananal 1998 n.a. 260,800 Brazil USA Forest 

rehabilitation and 

conservation

NSWSF + utilities 1998 1,300 10,000 Australia Australia Reforestation

World Bank Prototype

Carbon Fund

1998 n.a. n.a. International International Renewable 

energy and 

forestry

ProNatura—Peugeot 1999 n.a. n.a. Brazil France Forest 

rehabilitation and 

management

TNC Guaraquecaba 1999 n a N a. Brazil USA Forest 

conservation, 

rehabilitation and 

management

Totals/average 104,541 4,239,930
Status and Costs of Existing Forestry Sequestration Projects

C-4

References

1 Moura Costa P. Forestry-based greenhouse gas mitigation: a short history of market 

evolution. Global Greenhouse Emissions Trader Issue 8, Sept. 2000, p. 2-3.
D-1

D

PUBLISHED COSTS OF FORESTRY CARBON 

SEQUESTRATION

The following table is intended to give an indication of the range of published costs of forestry 

carbon sequestration and marginal values of land. In some of the references, best estimates or 

median costs are given along with ranges, which are shown here in brackets.

Table D-1

Published costs of forestry carbon sequestration

Ref. Description Country

Establishment

$/ha

Maintenance

$/ha y Land

1 Tree planting

Pacific—pasture

—cropland

—forest

South.East—pasture

—cropland

—forest

Other regions—pasture

—cropland

—forest

USA

531

445

610

166

147

299

155-484

141-373

287-378

$/ha/y

31

143

11

66

126

23

31-69

111-200

11-30

2 Afforestation

Reforestation

Argentina

USA

Argentina

Australia

Brazil

Canada

China

Germany

India

Malaysia

Mexico

South Africa

USSR

USA

988

255 (39-373)

1684 (662-1684)

347 (306-740)

637 (293-1207)

417 (335-513)

393 (329-410)

1391 (442-3662)

477 (220-1845)

303 (285-309)

402 (354-526)

952 (910-993)

83 (69-171)

256 (53-346)
Published Costs of Forestry Carbon Sequestration

D-2

Table D-2 (continued)

Published costs of forestry carbon sequestration

Ref. Description Country

Establishment

$/ha

Maintenance

$/ha y Land

3 Reforestation

Afforestation

Agroforestry

Boreal

Temperate

Tropical

Temperate

Tropical

324 (127-455)

357 (257-911)

450 (303-1183)

259 (41-444)

454 (255-699)

4 Reforestation, public land

Reforestation, private land

Reforestation

USA

USA

Tropical

680 (230-996)

680 (230-996)

395 (153-680)

0

84 $/ha/y

0

5 Plantations (softwood) Australia 428 establishment,

232 replanting after 

harvest

17 management

+19 fire insurance

250-1500 

$/ha

6 Plantations—lowland

Plantations—upland

UK 1762

3124

42-49

39-77

2980 $/ha

(950-

4750)

7 Plantations Industrialized

countries

Developing 

countries

680

400

20

20

8

9

Pasture land value

Cropland value

Pasture land value

Cropland value

USA

Note: Exchange rate assumed to be 2 Aus$/US$

References

1 Moulton, R.J., and K.R. Richards. Costs of sequestering carbon through tree planting and 

forest management in the United States. USDA Forest Service General Technical Report 

WO-58. December 1990.

2 Winjum, J.K., R.K. Dixon, and P.E. Schroeder. Forest management and carbon storage: an 

analysis of 12 key forest nations. Water, Air and Soil Pollution 70: 239-257, 1993.

3 Sedjo, R.A., et. al. The economics of managing carbon via forestry: assessment of existing 

studies. Environmental and Resource Economics 6: 139-165, 1995.

4 Sanghi, A.K., and K.S. Michael. Carbon sequestering through reforestation: cost and 

potential for New York. Air & Waste Management Association 84th Annual Meeting &

Exhibition, Vancouver, B.C., June 16-21, 1991.
Published Costs of Forestry Carbon Sequestration

D-3

5 Bureau of Transport and Communication Economics. Costs of carbon sequestration through 

afforestation: greenhouse gas emissions Australian transport. Working Paper 23 , 

Commonwealth of Australia, 1996, ISBN 0 642 24591 6.

6 ETSU. Full fuel-cycle study on power generation schemes incorporating the capture and 

disposal of carbon dioxide, volume 5: pulverised coal super-critical steam cycle with offset 

forestry CO2 mitigation. Report produced for the IEA Greenhouse Gas R&D Programme, 

October 1994.

7 Huotari, J.,S. Helynen, and M. Flyktman. Indirect biofixation of CO2, VTT Finland, report 

CRE/CON 1424 produced for IEA Greenhouse Gas R&D Programme. September 1993.

8 USDA agricultural land values. March 2000. www.usda.gov/nass/.

9 USDA agricultural cash rents. July 2000. www.usda.gov/nass/.

E-1

E

ESTIMATING ECONOMIC INCENTIVES REQUIRED TO 

ACHIEVE ADOPTION OF CONSERVATION-TILLAGE 

SYSTEMS IN SELECTED REGIONS OF THE UNITED 

STATES

Background

The purpose of this appendix is to provide first-approximation regional estimates of economic 

incentives ($/hectare) necessary to induce farmers to adopt reduced-tillage practices in regionally 

important cropping systems.

This appendix indicates the likely range of incentives required to achieve adoption of 

conservation-tillage systems and estimates incentives for the four regional case studies listed 

below. The four case studies were selected to (1) represent regions and cropping systems with 

the greatest amount of avoided GHG emissions that could be achieved by reducing tillage, and 

(2) illustrate the range of amounts per hectare and costs of avoided GHG emissions that could be 

achieved by reducing tillage.

1. Corn/soybean rotations in the Corn Belt being converted from intensive-till to either no-till or 

moderate-till. This case includes best-case and worst-case scenarios concerning effects of 

reducing tillage on N fertilizer requirements and on N2O emissions (Chapter19)

2. Continuous corn cropping systems in the Corn Belt being converted from intensive-till to 

either no-till or moderate-till. This case includes best-case and worst-case scenarios 

concerning effects of reducing tillage on N fertilizer requirements and on N2O emissions 

(Chapter 20)

3. Wheat/fallow rotations in the western Great Plains being converted from intensive-till to notill. This case includes variations in the amount of SOC sequestered and amount of N2O 

emissions due to reducing tillage (Chapter 21)

4. Intensive-till cotton to no-till cotton in the southeastern U.S. This case includes variations in 

the amount of SOC sequestered and amount of N2O emissions due to reducing tillage 

(Chapter 22)

Tillage-system budgets and yield distributions over time were used to estimate the incentive 

levels required to foster the adoption of conservation-tillage systems.
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-2

Budgeting Case Study Tillage Systems

Regional budgets were developed for tillage and cropping systems within each case study. 

Consistent budgeting methodology was used across tillage and cropping systems and regions as 

follows. The chemical and fertilizer application rates, seed, drying, insurance, and miscellaneous 

costs were based on state Agricultural Extension Service budgets whenever possible. In 

instances where published costs were not found (for example, for no-tillage corn/soybean 

chemical use in Indiana), regional specialists were contacted for the application/cost information.

The various state Agricultural Extension Service budgets employ varying methods for estimating 

machine variable costs; therefore, in this study, pre-harvest machinery operating costs were 

estimated using the standardized methodology of the American Society of Agricultural 

Engineers (ASAE) reported by the AAEA Task Force on Commodity Costs and Returns1. 

ASAE methodology uses estimated engineering equations along with typical machinery hours, 

age, size, and type to estimate all costs associated with repair, maintenance, fuel, and lubrication. 

Machinery hours, repair costs, and fuel costs for each piece of machinery were included in the 

budget. Total variable costs include both repair and fuel costs. Labor hours were based on the 

sum of all machinery time. It has become common practice to custom hire the harvesting field 

operation; therefore, this study used the lower-bound custom hire estimates from published state 

Agricultural Extension Service publications. Cotton budgets were the one exception where the 

range of custom-hire costs is so wide that there is a high rate of error in using the custom rate. 

Therefore, cotton-harvest cost was based on ASAE agricultural engineering estimates. Interest 

on pre-harvest costs was calculated using a 7.5 percent interest rate and labor costs were 

calculated at $8/hour for all regions. The sources of the individual budgets are listed in 

Attachment E-1 of Appendix E.

Estimating the Incentives to Adopt Reduced-Tillage Practices

Published research has shown that in many cases the mean net returns on reduced-tillage 

practices are equal to or greater than the returns from conventional tillage due to decreases in 

input costs, yet only 35 percent of agricultural lands have adopted conservation tillage practices2. 

One factor inhibiting the adoption of reduced tillage is the additional risk perceived by farmers 

and the perceived effects of these risks on net returns3,4,5,6. These risks include: the potential for 

reduced yields during initial years of adoption, the increased yield variability that reduced tillage 

practices may introduce, input use variability, and the human and/or physical capital investment 

that producers may incur. For this study, the impacts of these factors are represented in a 

probability distribution of net returns that farmers may perceive to be riskier than that associated 

with conventional practices. This study assumed that yield variation and production inputs are 

the primary factors of risk in cropland systems. The reduced tillage incentive must overcome the 

cost of net-returns variability before a risk adverse farmer will adopt reduced-tillage practices. 

Therefore, before finding the incentive level which will compel a switch to reduced tillage, 

farmer risk aversion behavior must be considered.

A methodological framework, referred to as the expected utility model7, provides a useful means 

for evaluating risk-return tradeoffs in agricultural production settings. In the expected utility 
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-3

model, the certainty equivalent (CE) for a risky decision is the return on a risk-free investment 

that makes the decision maker indifferent between the payoff from the risky decision and the 

payoff from the risk-free investment. The CE from one decision (x) is greater than for another 

decision (y) if, and only if, decision x yields greater utility than decision y, hence making 

decision x preferable over decision y by a risk averse individual. This relationship allows 

inferences to be made about the risk rankings of alternative agricultural practices. Finally, the 

CE is calculated in monetary units, and thus, it can be used as an indicator of the economic 

differences between conventional and alternative practices. For example, in terms of reduced 

tillage, the CE of profit per unit of land area can be approximated using Equation 18.

CE(NRx)=E(NRx)-λ/2Var(NRx) Eq. 1

Where E(NRx) is the expected net return for tillage practice x, ? is the value of the PrattArrow absolute risk aversion coefficient, and Var(NRx) is the variance of net returns for 

tillage practice x.

Freund9 has shown that the linear mean-variance objective function is consistent with the 

negative exponential utility function (assumes constant absolute risk aversion) and normally 

distributed profits. Thus, if CE(NRx)>CE(NRy), then practice x is a risk efficient strategy for a 

risk averse farmer because it provides a higher expected utility and would be adopted by the 

farmer. The difference in the CE profit, CEI=CE(NRx)-CE(NRy), provides an estimated 

monetary value, or incentive, that a risk averse farmer would need to be compensated for in order 

to adopt alternative tillage practice x.

Incentive levels for reduced-tillage practices were calculated using estimated certainty 

equivalents. The incentive level is defined as the monetary value that would make the producer 

indifferent between a certain choice (current practice) and a risky proposition (alternative tillage 

system).

Analytical Procedure

Incentive level estimation requires the estimation of the net returns of both the conventional 

tillage practices and the alternative reduced-tillage practices for each rotation and in each region. 

Total variable cost is given by the regional budgets for important combinations cropping and 

tillage systems. To estimate the gross revenue side of the equations, it is necessary to estimate 

the yield levels of the various regional cropping and tillage systems. Through an extensive 

literature and data search, 14 tillage experiments which studied the side-by-side yield effects of 

alternative tillage practices were identified which match the cases in this study. The most 

rigorous eight of these 14 studies were selected to represent the cases of interest. The reasons for 

the selection of these eight are reported in Attachment E-2 of Appendix E. This analysis uses the 

selected regional tillage experiments to estimate the tillage-system effects on yield, and 

therefore, gross revenue. The mean yield, standard deviation, and coefficients of variation and 

variable costs for each cropping system, tillage system, and region are presented in Table E-1.

In some cases, budgets were altered to appropriately fit the tillage practices in individual 

experiments. For example, in the central Corn Belt region, one study used chisel plow for the 
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-4

conventional tillage practice while another experiment used disking for the conventional tillage 

practice. The regional rotation budget was altered to accurately represent the conventional 

tillage practice of each experiment.

All budget and yield sources are indicated in Attachment E-2. To make comparisons across 

differing rotation periods, the total variable costs and total net returns of all rotations were 

converted to annual average costs and revenues. Hence, final incentive levels were also 

computed on an annul basis.

Risk is a significant factor to include in analysis of an incentive level which would prompt 

farmers to switch to an alternative practice. To include risk, an empirical distribution of yields 

from each experiment was computed for each experiment. Attachment 3 includes histograms of 

the distributions of the tillage systems compared. Using randomly selected yield levels from the 

empirical distributions of each experiment and using them in conjunction with their specific 

production cost budgets, the net returns were simulated 300 times. This procedure provided a 

probability distribution of net returns for each cropping and tillage system and region.
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-5

Table E-1

Yield mean, standard deviation, and coefficient of variation and variable costs at selected 

sites.

Region Rotation Site & Crop Tillage Mean

(Mg/ha)

St.Dev.

(Mg/ha)

Coef.

Var.

Variable 

Cost ($/ha)

IAAP- Intensive 9.15 2.57 0.28 434.92

corn No-Till 8.76 2.19 0.25 436.67

IAAP- Intensive 2.92 0.31 0.11 285.51

soybean No-Till 2.81 0.30 0.11 286.84

Intensive 9.93 1.57 0.16 434.92

Reduced 9.64 1.67 0.17 470.61

NERDFcorn

No-Till 9.71 1.85 0.19 436.67

Intensive 3.03 0.60 0.20 285.51

Reduced 2.92 0.62 0.21 320.80

NERDFsoybean

No-Till 2.96 0.62 0.21 286.84

Intensive 11.96 1.73 0.14 445.98

Reduced 12.06 1.71 0.14 447.49

ARCcorn

No-Till 11.61 1.74 0.15 413.53

Intensive 3.54 0.44 0.12 303.29

Reduced 3.39 0.46 0.14 311.47

Corn/

Soybean

ARCsoybean

No-Till 3.38 0.58 0.17 277.50

Intensive 8.88 2.04 0.23 488.17

Reduced 8.42 2.25 0.27 525.86

NERDFcorn

No-Till 8.18 2.11 0.26 491.93

Intensive 11.21 1.86 0.17 484.69

Reduced 11.09 1.68 0.15 485.68

Corn Belt

Cont. Corn

ARCcorn

No-Till 9.68 1.88 0.19 453.22

Cont. Intensive 5.37 1.45 0.27 290.60

Sorghum

RMFsorghum No-Till 5.24 1.32 0.25 320.95

RMF- Intensive 2.64 0.58 0.22 254.68

soybean No-Till 2.78 0.65 0.23 265.48

Intensive 7.83 1.31 0.17 273.06

Central 

Great 

Plains

Sorghum/

Soybean RMFsorghum No-Till 8.26 1.45 0.18 302.75

Wheat/ Intensive 2.56 0.76 0.30 203.31

Fallow

SWK1-

wheat No-Till 2.74 0.75 0.27 249.94

SWK1- Intensive 2.19 0.96 0.43 203.31

wheat No-Till 2.23 0.77 0.35 249.94

Intensive 3.59 1.24 0.34 273.06

Western 

Great 

Plains

Wheat/

Sorghum

/Fallow

SWK1-

sorghum No-Till 4.31 1.18 0.27 304.40

ALB- Intensive 8.15 1.52 0.19 377.22

corn No-Till 8.67 1.27 0.14 437.04

Intensive 2.40 0.53 0.22 253.84

Corn/

Soybean ALBsoybean No-Till 2.33 0.39 0.16 268.37

Intensive 8.94 3.03 0.34 694.34

Mississippi 

River

Corridor Cotton MEScotton No-Till 9.07 2.65 0.29 706.32
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-6

The distributions of net returns were analyzed using the SIMETAR© software to estimate 

confidence premium levels under risk aversion10. A risk aversion coefficient of .02 was selected 

for the analysis after reviewing the literature of risk aversion in agriculture at the per hectare 

level11, 5, 12. The confidence premium represents the payment that is necessary to equal the 

perceived net returns under risk aversion of the alternative practice; in other words, the risk 

premium is the incentive level necessary for producers to adopt the alternative practice. The 

estimated incentive levels by each case study region and rotation are presented in Table E-2.

Table E-2

Incentive levels under base yields and prices

Proposed

Switch

Region Site 

Abbv.*

and

Drainage

Conventional

Rotation

Alternative

Rotation

Incentive Level 

Under Base 

Yields and 

Prices ($/ha)

IAAP

Poor

Corn/Soybean Corn/Soybean 25.18

NERDF

Well

Corn/Soybean Corn/Soybean 20.51

NERDF

Well

Cont. Corn Cont. Corn 66.05

Corn Belt

ARC

Poor

Cont. Corn Cont. Corn 99.98

RMF Cont. Soybean Cont. Soybean 6.84

RMF Sorghum/Soybean Sorghum/Soybean 0.74

Central Great 

Plains

NAF Cont. Sorghum Cont. Sorghum 36.1

SWK1 Wheat/Fallow Wheat/Fallow 14.63

Western 

Great Plains SWK1 Wheat /Fallow Wheat/Sorghum/

Fallow

15.17

ALB Corn/Soybean Corn/Soybean 19.67

Intensive Till

To

No-Till

Mississippi 

River 

Corridor MES Cont. Cotton Cont. Cotton 23.2

ARC

Poor

Corn/Soybean Corn/Soybean 15.22

NERDF

Well

Corn/Soybean Corn/Soybean 56.17

ARC

Poor

Cont. Corn Cont. Corn 3.51

Intensive Till

To

Reduced Till

(Ridge Till)

Corn Belt

NERDF

Well

Cont. Corn Cont. Corn 101.26

* Note: Definition of site abbreviations can be found in Attachment E-2.
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-7

Discussion of Incentive Level Results

One would expect that the incentive level for switching from intensive-till to no-till would be 

higher for poorly drained soils than for well-drained soils, and this is the result in the Corn Belt 

rotations of corn/soybean and continuous corn. The corn/soybean incentive is $25/ha for poorly 

drained soil and $20/ha for well-drained soil. The continuous corn incentive for poorly drained 

soils is $100/ha and $66/ha for well-drained soils. In the continuous corn rotation, no-till yields 

decline considerably, thereby making no-till continuous corn very uncompetitive and resulting in 

very high incentive levels. Risks of lower and more variable yields associated with no-till 

continuous corn have been lowered somewhat in recent years via improvements in technology 

and experience.

When looking at the switch from intensive to reduced tillage, we see that the incentive levels are 

much higher in the well-drained soil type than the poorly drained. The poor soil drainage studies 

saw little yield drop in the reduced-tillage system, and in some cases, even increased yield (corn 

in corn/soybean rotation, ARC site). The reduced tillage systems in well-drained soils lost yield 

in all sites. One hypothesis for this result is that the poorer soils are simultaneously reaping the 

advantages of both less tillage and better drainage. The reduced tillage system studied in these 

experiments is the ridge-till system, where a field cultivator is used to build high rows in which 

the crops are planted. This would give the needed drainage a minimum of tillage intrusion.

In the central Great Plains, the profitability of intensive- and no-tillage sorghum/soybean 

rotations are about the same, with only a $0.74/ha incentive level estimated to bring no-till into 

production. Continuous sorghum has a very high incentive level of $36/ha. The mean yields are 

fairly close, but the increased cost of no-till sorghum causes the high-incentive level in this case.

In the wheat/fallow and wheat/sorghum/fallow rotations of the western Great Plains, the yields 

with no-till were actually higher than with intensive-till and yield variations with no-till were 

less. The incentive level of around $15/ha is a result of the higher cost of no-till in these 

systems.

In the Mississippi River Corridor Region, the experimental results show that the switch from 

corn/soybean to corn/soybean-wheat would take an incentive level of $74/ha. The wheat yield of

the analysis is from a separate continuous wheat rotation in the region and this results in a mean 

yield of 4 tonne/ha, which is very low. The poor returns from wheat combined with the extra 

cost of the added crop, lead to this very high-incentive level. It is assumed that this is a very 

extreme estimate of the incentive level. By leaving wheat out of the rotation, the analysis shows 

an incentive level of $20/ha for switching to no-till corn/soybean.

The experimental yield results of continuous cotton in the Mississippi River Corridor Region 

show no-till slightly higher and with less variation. The $23/ha incentive level is, therefore, the 

result of the higher cost ($12/ha) and risk of no-till.
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-8

Sensitivity of Incentives to Changes in Revenue

The incentive levels described above (Table E-2) are based on current prices and, therefore,

revenue. As prices (and revenue) change, the incentive levels will also change. Revenue at four 

other levels (10 percent and 20 percent more and less than current prices) was estimated using 

the same methodology as above. This enables revenue sensitivity equations to be estimated, 

which represent the change in incentive as the level of revenue changes. All resulting equations 

are listed in Table E-5. As prices change, the percentage change in revenue from the baseline 

level of revenue can be computed as represented in Equation 2. The baseline yields and 

revenues are listed in Table E-3, and the baseline prices are reported in Table E-4. The new 

incentive level can then be estimated using the computed equations which follow the form of 

Equation 3 and are listed in Table E-5.

Equation 2: venue P Y J Base venue Base venue

n

i

% Re (( ( i*

i

) / ) Re ) / Re

1

Δ = ∑ −

=

,

where n is the number of crops and J is the number of years in rotation.

Equation 3: Incentive level = (% Re )

0 1 b + b Δ venue

Response of the various rotation incentive levels to changes in revenue varies considerably. 

Most follow the general trend that as revenues increase, incentive levels increase. But in seven 

rotations, incentive levels decrease as total revenue increases. This is due to two separate causes. 

In some cases, the variance in yields of conventional tillage is more than the variance of notillage yields. This causes the relative amount of revenues under conventional tillage to become 

more “risky” as revenues increase. Therefore, to a risk adverse producer, conventional tillage 

becomes less attractive. The other cause occurs in the comparisons to the conventional 

wheat/fallow rotation of the western Great Plains. The cost of the conventional wheat/fallow 

rotation is low compared to the alternative no-tillage practices. At low prices (revenue), the 

extra cost of the alternative practices is not offset by extra revenue, but as crop prices increase, 

the increase in mean expected total revenue offsets the higher cost of the rotation practice. This 

makes the alternative practices relatively more attractive at higher revenues; therefore, the 

incentive decreases.

The estimated incentive levels reported in Table E-2 are to be considered as the average 

incentives required by producers to adopt the alternative tillage system at baseline levels of 

commodity prices. However, more than likely the incentive would vary across farm sizes, types 

of operators, and other factors, all variables which are beyond the scope of this study.
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-9

Table E-3

Base yields and revenues by experimental site and rotation.

Proposed

Switch

Region Site 

Abbv.*

Rotation

and

Drainage

Base Yields

(Mg/ha)

Base 

Revenue 

($/ha/yr)

IAAP Corn/ Corn 9.15

Soybeans: poor Soybeans 2.96

646.41

NERDF Corn/ Corn 9.95

Soybeans: well Soybeans 3.03

684.22

NERDF Cont. Corn: well Corn 8.94 690.15

Corn Belt

ARC Cont. Corn: poor Corn 11.23 866.58

RMF Cont. Soybeans Soybeans 2.69 533.74

RMF Sorghum/ Sorghum 8

Soybeans Soybeans 2.69

560.92

Central 

Great Plains

NAF Cont. Sorghum Sorghum 5.38 395.36

SWK1 Wheat/

Fallow

Wheat 1.95 102.13

Western 

Great Plains SWK1 Wheat/Sorghum/

Fallow

Wheat 1.95 102.13

ALB Corn/ Corn 8.2

Soybeans Soybeans 2.42

556.72

Intensive 

Till

To

No-Till

Mississippi 

River 

Corridor

MES Cont. Cotton Cotton 0.97 1,107.18

ARC Corn/ Corn 12.04

Soybeans: poor Soybeans 3.56

818.02

NERDF Corn/ Corn 9.95

Soybeans: well Soybeans 3.03

684.22

ARC Cont. Corn: poor Corn 11.23 886.58

Intensive 

Till

To

Reduced 

Till

Corn Belt

NERDF Cont. Corn: well Corn 8.94 690.15

* Note: Definition of site abbreviations can be found in Attachment 2.
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-10

Table E-4

Base prices

Crop Price($/Mg)

Corn 77.16

Soybeans 198.41

Wheat 104.72

Sorghum 73.49

Cotton 1,144.62

Table E-5

Revenue sensitivity equations

Proposed

Switch

Region Site* and

Drainage

Conventional

Rotation

No-Till Rotation Incentive-Level Equation

IAAP

Poor

Corn/Soybean Corn/Soybean 25.18t+0.067 t (%ΔRev) 

r

2

=.99

NERDF

well

Corn/Soybean Corn/Soybean 20.51t+0.533 t (%ΔRev) 

r

2

=.99

NERDF

well

Cont. Corn Cont. Corn 66.05t+0.681 t (%ΔRev) 

r

2

=.99

Corn

Belt

ARC

poor

Cont. Corn Cont. Corn 99.98t+1.525 t (%ΔRev) 

r

2

=.99

RMF Cont. 

Soybeans

Cont. Soybean 6.84t

+0.00 t (%ΔRev) 

r

2

=.99

RMF Sorghum/

Soybean

Sorghum/

Soybean

0.74t-0.163 t (%ΔRev) 

 r2=.99

Central 

Great Plains

NAF Cont. Sorghum Cont. Sorghum 36.10t+0.052 t (%ΔRev) 

r

2

=.99

SWK1 Wheat/

Fallow

Wheat/

Fallow

14.63t-0.075 t (%ΔRev) 

r

2

=.99

Western 

Great Plains

SWK1 Wheat/

Fallow

Wheat/Sorghum/

Fallow

15.17t-0.684 t (%ΔRev) 

r

2

=.99

ALB Corn/

Soybean

Corn/

Soybean

19.67t-0.216 t (%ΔRev)

r

2

=.99

Intensive Till 

to

No-Till

Mississippi 

River 

Corridor MES Cont. Cotton Cont. Cotton 23.2t

+0.708 t (%ΔRev) 

r

2

=.99

ARC

Poor

Corn/Soybean Corn/Soybean 15.22t

+0.109 t (%ΔRev) 

r

2

=.99

NERDF

Well

Corn/Soybean Corn/Soybean 56.17t

+0.200 t (%ΔRev) 

r

2

=.99

ARC

Poor

Cont. Corn Cont. Corn 3.51t

-0.068 t (%ΔRev) 

r

2

=.99

Intensive Till 

to Reduced 

Till

Corn

Belt

NERDF

Well

Cont. Corn Cont. Corn 101.26t

+0.897 t (%ΔRev) 

r

2

=.99

* Note: Definition of site abbreviations can be found in Attachment E-2. 

t=all coefficients significant at the 0.00 level of significance.
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-11

References Cited

1 American Agricultural Economics Association (AAEA), 1998. Commodity Costs and 

Returns Estimation Handbook. A Report of the AAEA Task Force on Commodity Costs 

and Returns. Ames, Iowa.

2 Sandretto, C. 2001. Conservation tillage firmly planted in U.S. agriculture. Agricultural 

Outlook March 2001. Economic Research Service, U.S. Department of Agriculture, 

Washington, D.C.

3 Nowak, P. J., and P. F. Korsching. 1985. Conservation tillage: revolution or evolution? 

Journal of Soil and Water Conservation 40: 199-201.

4 Prato, T. 1990. Economic feasibility of conservation tillage with stochastic yields and 

erosion rates. North Central Journal of Agricultural Economics 12: 333-334.

5 Larson, J. A., R. K. Roberts, D. D. Tyler, B. N. Duck, and S. P. Slinsky. 1998. Nitrogen 

fixing winter cover crops and production risk: a case study for no tillage corn. Journal of 

Agricultural and Applied Economics 30: 163-174.

6 Larson, J. A., E. C..Jaenicke, R. K. Roberts, and D. D. Tyler. 2001. Risk effects of 

alternative winter cover crop, tillage, and nitrogen fertilization systems in cotton production. 

Journal of Agricultural and Applied Economics 33: 445-457.

7 Meyer, J. 2002. Expected utility model as a paradigm for decision making in agriculture. 

Chapter 1 in R. E. Just and R. Pope (eds.). The Role of Risk in Agriculture. Kluwer 

Academic Publishers, Norwell, MA.

8 Robison, L. J., and P. J. Barry. 1987. The Competitive Firm’s Response to Risk. 

Macmillan Publishers, New York.

9 Freund,R. J. 1956. Introduction of risk into a programming model. Econometrica 24: 257-

263.

10 Richardson, James W. 2002. Simulation for Applied Risk Management, with an 

Introduction to the Software Package SIMETAR©: Simulation for Excel to Analyze Risk. 

Department of Agricultural Economics, Texas A&M University.

11 Cochran, Mark J. “Stochastic Dominance: The state of the art in agricultural economics.”

Proceeding of a Seminar Sponsored by Southern Regional Project S-180, Tampa, Florida, 

March 23-26, 1986. Dept of Ag Economics, Washington State Univ. Pullman, Washington.

12 Williams, Jeffery R. “A Stochastic Dominance Analysis of Tillage and Crop Insurance

Practices in a Semi-arid Region.” American Journal of Agricultural Economics 70.1 (Feb 

1988): 112-120.
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-12

13 Sunding, D., and D. Zilberman. 2000. The Agricultural Innovation Process: Research and 

Technology Adoption in a Changing Agricultural Sector. Chapter 5, Handbook of 

Agricultural Economics, Vol. 1A: Agricultural Production, edited by B. L.Gardner and 

G. C. Rausser. Amsterdam: Elsevier Science Publications.

14 Kurkalova, Lyubov, Catherine King, and Junhua Zhao. 2001. The Subsidy for Adopting 

Conservation Tillage: Estimation from Observed Behavior. Working Paper 01-WP 286, 

CARD, Iowa State University. Paper Presented at the AAEA 2001 Annual Meeting in 

Chicago.

15 Griliches, Zvi. 1957. Hybrid Corn: An Exploration in the Economics of Technological 

Change. Econometrica 25.4 (October 1957): 501-522.
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-13

Attachment E-1 of Appendix E: Budget and Yield Sources

Case#1: Convent. corn/soybeans to no till

Case#2: Convent. continuous corn to no-till

Budgets:

Iowa: Ag Decision Maker, Mike Duffy, January 2002. Seed, Fert, insurance, misc, and Dry 

APAC Budgets Pre Harvest Machinery, chemicals 

Indiana: Dobbins, C.L., et al. "2002 Purdue Crop Guide For conventional fertilizer app rates,drying insurance, misc.

Craig Dobbins, Purdue Ag Econ Dept. Herbicide

Ohio State Crop Budgets Herbicide

APAC Budgets Pre-harvest Machinery

Indiana Custom Rates, D.H.Doster Harvest cost

Iowa

NERDF, Nashua, IA

Data: Research Farm Manager, Ken Pecinovsky [kennethp@willowtree.com]

Site: North-East Research and Demonstration Farm, located near Nashua, Iowa in the northeast corner of the state

Soil: Kenyon Loam, Tile Drained=Well Drained

Years: 1978-2001

IAAP, Burlington, IA

Data: Brown, H.J. "Tillage System Effects on Crop Growth and Production Costs for a Corn-Sobean Rotation",

J.Prod.Agic. Vol 2 no 3 1989

Site: Iowa Army Ammunition Plant near Burlington, IA, Southeastern part

Soil: Poorly drained Taintor/somewhat poorly drained Mahaska...silty clay loams

Years: 1980-1990

SERDF, Crawfordsville, IA

Data: Southeast Research and Demonstration Farm--2000 Annual Progress Reports, Farm Manager, Kevin VanDee 641-435-4864

Site: Southeast Research and Demonstration Farm, Crawfordsville, IA,

Soil: Poory Drained Kalona Soil, 2) Well Drained Nira Soils

Years: 1980-2001

Indiana

ARC, West Lafayette, IN

Data: Cropping Systems Research Report 2001, T.D. West, T.J. Vyn, Department of Agronomy, Purdue Univesity

Site: Purdue Agronomy Research Center(ARC), West Lafayette, West-Central Indiana

Soil: Chalmers Silty clay loam soil (4%OM),"Poorly Drained"

Years: 1975-2001

PPAC, Wanatah, IN

Data: Cropping Systems Research Report 2001, T.D. West, T.J. Vyn, Department of Agronomy, Purdue University

Site: Pinney-Purdue agricultural center(PPAC), Wanatah, IN, Northern Indiana

Soil: Sebewa Loam, "Very Poorly Drained"

Years: 1997-2001 
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-14

Case#3: Convent. sorghum, soybeans and sorghum/soybeans to no-till.

Budgets:

Kansas: Kansas State Farm Management Guides seed, fert, chemicals,misc

APAC Budgets Pre-harvest machinery

Kansas State Custom Rates Harvest cost

NAF, Manhattan, KS

Data: LONG-TERM NITROGEN MANAGEMENT AND TILLAGE EFFECTS ON GRAIN SORGHUM, 

R.E. Lamond, G.M. Pierzynski and D.A. Whitney

Site: North Agronomy Farm, Manhattan, KS

Soil: Smolan silty clay loam, 2.3% organic matter, 6.1 pH, Bray-1 P of 26 ppm and exchangeable K of 305 ppm.

Years: 1982-2001

USE THIS DATA FOR A)SORGHUM CONTINUOUS CONVENT TO SORGHUM CONT. NOTILL

RMF, Lincoln, NE

Data: Dickey,Elbert C., Paul Jasa,Robert Grisso,

Long Term Tillage Effects on Grain Yield and Soil Properties in a Soybean/Grain Sorghum RotationJ.Prod.Ag.Vol7,no4,1994

Site: Rogers Memorila Farm, near Lincoln, Nebraska

Soil: Sharpsburg silty clay loam (fine, montmorillonitic, mesic Typic Argiudolls)

Years: 1981-1990

USE THIS DATA FOR A)SOYBEAN/SORGHUM CONVENT TO SOYBEAN/SORGHUM NOTILL, 

B) SOYBEAN CONTINUOUS CONVENT TO SOYBEAN CONT. NOTILL

Case#4: Convent. wheat/fallow to no-till in Western Great Plains

Case#5: Convent. wheat/fallow to wheat/sorghum/fallow and wheat/corn/fallow in Wester Great Plains

Budgets:

Kansas: Kansas State Farm Management Guides seed, fert, chemicals,misc

APAC Budgets Pre-harvest machinery

Kansas State Custom Rates Harvest cost

RHBES, Fort Hays Exp. Station, KS

Fort Hays Branch Experiment Station

Data: Jeff Williams sent data from a grad thesis.

Site: Fort Hays, KS Exp Station

Soil:

Years: 1976-1986

USE THIS DATA FOR WH/FALLOW CONVENTIONAL TO A)WH/FALLOW/SORGHUM NO TILL AND B)WH/FALLOW NO TILL

SWK1, Garden City, KS

Data: "Cropping System and Tillage Effects on Available Soil Water and Yield of Grain sorghum and Winter Wheat",

Norwood,C.A. et al., J.Prod.Agric.,Vol.3,no.3.1990.87-91:

Norwood,"An Economic Comparisn of the wheat fallow and wheat sorghum fallow cropping systems"

J.Pro.Ag.Vol6,No2,april-june 1993.

Site: Southwest Kansas Res.-Extension Ctr.,Garden City, KS

Soil: Satanta loam(fine-loamy,mixed,mesic Aridic Agriustoll)ph of 7.0, organ matter of 1.3%

Years: 1979-1987

USE THIS DATA FOR A)WH/FALLOW CONVENT TO WH/FALLOW/SORGHUM NO TILL, 

B)WH/FALLOW CONVENT TO WH/FALLOW NOTILL

SWK2, Garden City, KS--not using now

Data: POST-HARVEST WEED CONTROL IN A WHEAT-FALLOW ROTATION,Alan Schlegel and Troy Dumler

Site: Southwest Kansas Res.-Extension Ctr.,Garden City, KS

Soil: Satanta loam(fine-loamy,mixed,mesic Aridic Agriustoll)ph of 7.0, organ matter of 1.3%

Years: 1996-2001

USE THIS DATA FOR A)WH/FALLOW CONVENT TO WH/FALLOW NOTILL
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-15

Case#6: Convent Corn/Soybean to Corn/wheat-soybeans in Mid South

Budgets:

Tennessee: TN Field Crop Budgets, Ag. Ext. Service all but custom

TN Custom Rates,Ag. Ext. Service Harvest Costs

ALB--soybean/corn

Data: Edwards, J.H., D.L. Thurlow, and J.T. Eason. 1988. Influence of Tillage and Crop Rotation on Yields of Corn, Soybean, and Wheat. 

Agronomy Journal 80:76-80.

Site: Sand Mt Substation, Crossville, AL

Soil: fine sandy loam, well drained

Years: 1980-1984

ALB--soybean/corn

Data: Tillage and wheat Production, Touchton, Joseph t., etal. Department of Agronomy, Auburn University.

Site: Monroeville, AL

Soil: Sumpter soil

Years: 1984-1992

Case#7: Convent Cotton to No-Till in Mid South.

Budgets:

Tennessee: TN Field Crop Budgets, Ag. Ext. Service all 

Not using custom rates for cotton. The custom price spread is too large and would lead to

either severe over or under estimation.

MES, Milan, TN

Data: Bradley,J.F. "Success with No-Till Cotton" in Arkansas Agricultural Experiment Station Special Publication 160,

 pp.46-48;revised 01-25-95

Site: Milan, Tennessee

Soil: Grenada Silt Loam

Years: 1981-1994
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-16

Attachment E-2 of Appendix E: Site Selection

Corn Belt

Corn/Soybean Rotation

Well-Drained Soil

There are two experimental data sets from “well-drained soils,” the NERDF and SERDF sites. 

Both NERDF and SERDF experiments are carried out in small test plots in Iowa. The “welldrained” soil in the SERDF experiment was on a 2-9 percent slope. The lesser slopes in this 

region are poorly drained. The NERDF experiment “well-drained” soils are on a 1-2 percent

slope and may be more representative of “well-drained” soils for the region as a whole. NERDF 

has 24 years of data compared to SERDF’s 11 years. For these reasons, the NERDF site is 

preferred as the representative for well-drained soils.

Corn/Soybean Rotation

Poorly drained Soil

There are four experimental data sets from “poorly drained soils”: SERDF, IAAP, PPAC, and 

ARC. The PPAC site is excluded because it only has five years of data. The SERDF, ARC, and 

IAAP sites have 11, 24, and 10 years of data, respectively. Both the SERDF and ARC 

experiments were conducted on small test plots. The IAAP site is unique in that it was 

conducted on a 432 acre site; therefore, standard field equipment could be used. Although the 

ARC site has more years of data, the uniqueness of the scale of the IAAP site makes it preferable 

in the switch to no-tillage. The IAAP site has no reduced tillage data; therefore, we use the ARC 

site for the switch to reduced tillage.

Continuous Corn

Well Drained Soil

As mentioned above, the “well-drained” soil in the SERDF experiment was on a 2-9 percent

slope. The lesser slopes in this region are poorly drained. The NERDF experiment “welldrained” soils are on a 1-2 percent slope and may be more representative of “well-drained” soils 

for the region as a whole. NERDF has 24 years of data compared to SERDF’s 11 years. For 

these reasons, the NERDF site is preferred as the representative for well-drained soils.

Continuous Corn

Poorly Drained Soil

There are three continuous corn data sets from poorly drained soils, SERDF, PPAC and ARC 

sites. As stated above, an eastern experiment would be more preferred, as it may represent the 

more common poorly drained soils of the eastern part of the Corn Belt. The ARC site has 

24 years of data compared to PPAC’s five years of data. Also, the PPAC site is very far north 

and may not be representative of the region. For these reasons, the ARC site is preferred.

Western Great Plains

Wheat/fallow and wheat/sorghum/fallow

FHBES experiment data was sent from a graduate research paper. Much of the information 

concerning the experiment is missing, such as soil type, experiment plot size, and chemical use. 

The SWK1 site has more specific information. Additionally, the SWK1 site is further west, in 

Garden City, as opposed to Hays, Kansas. For these reasons, the SWK1 site is preferred.
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-17

Attachment E-3 of Appendix E: Yield Comparisons

50 60 70 80 90 100 110 120 130 140 150 160 170 180

Yield

0

5

10

15

20

25

Fre

q

u

e

n

cy

Corn Belt: Corn/Soybean Rotation

Iowa Army Ammunition Plant (IAAP)

Burlington, IA

Corn Yield, poorly drained

Intensive mean=136.7

No-Till mean=131.0
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-18

28 30 32 34 36 38 40 42 44 46 48 50 52 54

Yield

0

10

20

30

40

fre

q

u

e

n

c

y

Corn Belt: Corn/Soybean Rotation

Iowa Army Ammunition Plant (IAAP)

Burlington, IA

Soybean Yield, poorly drained

Intensive mean=43.5

No-Till mean=42.0
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-19

50 60 70 80 90 100 110 120 130 140 150 160

Yield

0

4

8

12

16

fre

q

u

e

n

c

y

Corn Belt: Continuous Corn

Northeast Research and Demo Farm (NERDF)

Nashua, IA

Corn Yield, well drained

Intensive mean=132.7

No-Till mean=122.2
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-20

20 24 28 32 36 40 44 48 52 56 60 64 68

Yield

0

10

20

30

fre

q

u

e

n

cy

Corn Belt: Corn/Soybean Rotation

Northeast Research and Demo Farm (NERDF)

Nashua, IA

Soybean Yield, well drained

Intensive mean=45.3

No-Till mean=44.3
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-21

50 60 70 80 90 100 110 120 130 140 150 160

Yield

0

4

8

12

16

fre

q

u

e

n

c

y

Corn Belt: Continuous Corn

Northeast Research and Demo Farm (NERDF)

Nashua, IA

Corn Yield, well drained

Intensive mean=132.7

No-Till mean=122.2
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-22

70 80 90 100 110 120 130 140 150 160 170 180 190 200

Yield

0

5

10

15

20

25

fre

q

u

e

n

cy

Corn Belt: Continuous Corn

Purdue Agronomy Research Center (ARC)

W. Lafayette, IN

Corn Yield, poorly drained

Intensive mean=167.5

No-Till mean=144.7
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-23

120 130 140 150 160 170 180 190 200 210 220

Yield

0

5

10

15

20

25

fre

q

u

e

n

cy

Corn Belt: Corn/Soybean Rotation

Purdue Agronomy Research Center(ARC)

W. Lafayette, IN

Corn Yield, poorly drained

Intensive mean=178.8

Reduced mean=180.4
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-24

32 36 40 44 48 52 56 60 64 68

Yield

0

5

10

15

20

25

fre

q

u

e

n

c

y

Corn Belt: Corn/Soybean Rotation

Purdue Agronomy Research Center (ARC)

W. Lafayette, IN

Soybean Yield, poorly drained

Intensive mean=52.9

Reduced mean=50.7
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-25

80 90 100 110 120 130 140 150 160 170 180 190 200

Yield

0

4

8

12

16

20

fre

q

u

e

n

cy

Corn Belt: Corn/Soybean Rotation

Northeast Research and Demo Farm (NERDF)

Nashua, IA

Corn Yield, well drained

Intensive mean=148.4

Reduced mean=144.1
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-26

20 24 28 32 36 40 44 48 52 56 60 64 68

Yield

0

10

20

30

fre

q

u

e

n

c

y

Corn Belt: Corn/Soybean Rotation

Northeast Research and Demo Farm (NERDF)

Nashua, IA

Soybean Yield, well drained

Intensive mean=45.3

Reduced mean=43.6
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-27

100 110 120 130 140 150 160 170 180 190 200 210 220 230

Yield

0

5

10

15

20

25

fre

q

u

e

n

c

y

Corn Belt: Continuous Corn

Purdue Agronomy Research Center (ARC)

W. Lafayette, IN

Corn Yield, poorly drained

Intensive mean=168.8

Reduced Mean=165.8
Estimating Economic Incentives Requires to Achieve Adoption of Conservation-Tillage Systems

 in Selected Regions of the United States

E-28

30 40 50 60 70 80 90 100 110 120 130 140 150 160

Yield

0

4

8

12

fre

q

u

e

n

cy

Corn Belt: Continuous Corn

Northeast Research and Demo Farm(NERDF)

Nashua, IA

Corn Yield, well drained

Intensive=132.7

Reduced mean=125.9
F-1

F

ESTIMATING STATE-LEVEL SOC RESPONSES

The purpose of this appendix is to describe how state-level SOC response curves were developed 

for Indiana and Iowa. The state curves were developed from carbon storage projects recently 

completed by Smith, et al.1, in Indiana, and Brenner, et al.2, in Iowa. These projects provided 

state-level estimates of increased SOC as a result of switching corn/soybean rotations from 

intensive-till to moderate-till or no-till. These were point estimates for 10 years after switching 

tillage-systems. These projects provided county-level point estimates for 10 and 20 years after 

switching tillage systems. At least two data points in addition to the zero-zero point are needed 

to characterize the nonlinear increase in SOC that results from reducing tillage. Therefore, 

county-level SOC response data (10- and 20-year data points) were selected from counties that 

represent the state-wide range of SOC responses and also provide a mean response that passes 

through the state-wide mean for 10 years after switching tillage. In Indiana, Allen and Gibson 

Counties represented the range of SOC responses and the mean of curves for Tippecanoe and 

Gibson Counties were used to represent the state mean (Table F-1). This interpolation process 

was performed for corn/soybean rotations switched from intensive-till to both moderate-till and 

no-till and resulted in state curves that passed through or very nearly through the state mean 

10-year data points for corn/soybean rotations. Curves for the same counties were used to 

develop state curves for continuous corn switched from intensive-till to both moderate-till and 

no-till.

More specifically, a quadric regression was performed for each county-level set of data points 

(i.e., for 0, 10, and 20 years). The quadratic responses were transformed as described by Bock,

et al.3, to provide SOC as a function of the maximum increase in SOC (?SOCs) and a slope or 

efficiency factor (E). An example of this process for the first SOC response in Table F-1 is 

illustrated in Figure F-1.

For both Indiana (Figure F-2) and Iowa (Figure F-3), there was a linear relationship between E 

and ?SOCs for the range of curves generated. Therefore, the mean of two or more curves was 

calculated using the mean of E values for the curves being averaged and the mean of ?SOCs 

values for the curves being averaged.

Finally, state curves were calculated for corn/soybean rotations switched from intensive-till to 

moderate-till or no-till using weighted averages for the E and ?SOCs parameters for hydric and 

non-hydric soils. In Indiana, 79 percent of the soils are non-hydric and 21 percent of the soils are 

hydric (personal communication, John Brenner, NRCS). The same process was conducted for 

continuous corn. Results of these weighted averages are at the end of Table F-1.

The same overall process was performed for Iowa (Table F-2). Buchanan and Greene Counties 

were used to represent the range of responses in Iowa and the mean of curves from Buchanan, 
Estimating State-Level SOC Responses

F-2

Pottawattamoe, and Greene Counties were used to represent the state mean. In Iowa, 77 percent 

of the soils are non-hydric and 23 percent of the soils are hydric (personal communication, John 

Brenner, NRCS).

State County Rotation Tillage Hydric Texture

IN Allen C/SB No N L

Year ?SOC

0 0

10 5100 from IN carbon storage project (Table 1)

20 8100 from IN carbon storage project (Table 1)

?SOC=B1*Years+B2*Years*Years=(10*E)*Years-(25*E*E/?SOCs)*Years*Years

B1= 615 from graph

B2= -10.5 from graph

E= 61.5 =slope factor for best-fit quadratic response=B1/10

?SOCs= 9005 =increase in SOC to reach the new steady-state SOC=(-25*E*E)/B2

Ys= 29.3 =years to reach the new steady-state SOC=?SOCs/(5*E)

Intensive-till to No-till

y = -10.5x2 + 615x

R

2

 = 1

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0 5 10 15 20 25

Years after switching tillage

?SOC, kg C/ha

Figure F-1

Quadratic regression and transformation procedure

for SOC response to reducing tillage
Estimating State-Level SOC Responses

F-3

C/SB and Continuous Corn

line forced through 0/0

y = 0.007x

R

2

 = 0.9658

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

0 5000 10000 15000 20000

?SOCs, kg/ha

E

Figure F-2

Linear relationship between E and ?SOCs for the Indiana data from Table F-1

C/SB and Continuous Corn

line forced through 0/0

y = 0.0069x

R

2

 = 0.9852

0.0

20.0

40.0

60.0

80.0

100.0

120.0

0 2000 4000 6000 8000 10000 12000 14000 16000

?SOCs, kg/ha

E

Figure F-3

Linear relationship between E and ?SOCs for the Iowa data from Table F-2
Estimating State-Level SOC Responses

F-4

Table F-1

Increases in SOC due to switching from intensive-till to no-till or moderate till in Indiana

E Years to

State County Rotation Tillage Hydric Texture 10 years 20 years ?SOCs (slope factor) ?SOCs (Ys)

kg C/ha

IN Allen C/SB No N L 5100 8100 9005 61.5 29.3

IN Allen C/SB No N CL 5000 8000 9000 60.0 30.0

Mean 9003 60.8 29.7

IN Allen C/SB No Y L 4300 6800 7511 52.0 28.9

IN Allen C/SB No Y CL 4200 6700 7501 50.5 29.7

Mean 7506 51.3 29.3

IN Allen C/SB Moderate N L 3500 5400 5778 43.0 26.9

IN Allen C/SB Moderate N CL 3500 5600 6300 42.0 30.0

Mean 6039 42.5 28.5

IN Allen C/SB Moderate Y L 2800 4300 4578 34.5 26.5

IN Allen C/SB Moderate Y CL 2900 4600 5104 35.0 29.2

Mean 4841 34.8 27.9

IN Allen C No N L 10200 15900 17223 124.5 27.7

IN Allen C No N CL 9900 15700 17415 119.5 29.1

Mean 17319 122.0 28.4

IN Allen C No Y L 7500 11700 12685 91.5 27.7

IN Allen C No Y CL 7500 11600 12447 92.0 27.1

Mean 12566 91.8 27.4

IN Allen C Moderate N L 6900 10700 11517 84.5 27.3

IN Allen C Moderate N CL 7300 11500 12633 88.5 28.5

Mean 12075 86.5 27.9

IN Allen C Moderate Y L 4700 7400 8123 57.0 28.5

IN Allen C Moderate Y CL 5200 8200 9020 63.0 28.6

Mean 8572 60.0 28.6

IN Tippecanoe C/SB No N L 4900 7700 8429 59.5 28.3

IN Tippecanoe C/SB No N CL 4800 7500 8148 58.5 27.9

Mean 8289 59.0 28.1

IN Tippecanoe C/SB No Y L 4000 6400 7200 48.0 30.0

IN Tippecanoe C/SB No Y CL 4000 6300 6918 48.5 28.5

Mean 7059 48.3 29.3

IN Tippecanoe C/SB Moderate N L 3500 5300 5565 43.5 25.6

IN Tippecanoe C/SB Moderate N CL 3500 5400 5778 43.0 26.9

Mean 5672 43.3 26.3

IN Tippecanoe C/SB Moderate Y L 2700 4200 4538 33.0 27.5

IN Tippecanoe C/SB Moderate Y CL 2800 4400 4817 34.0 28.3

Mean 4678 33.5 27.9

IN Tippecanoe C No N L 8200 13500 16056 96.5 33.3

IN Tippecanoe C No N CL 7700 12700 15167 90.5 33.5

Mean 15612 93.5 33.4

IN Tippecanoe C No Y L 6400 9900 10625 78.5 27.1

IN Tippecanoe C No Y CL 6000 9000 9375 75.0 25.0

Mean 10000 76.8 26.1

IN Tippecanoe C Moderate N L 6600 10400 11429 80.0 28.6

IN Tippecanoe C Moderate N CL 7200 11400 12615 87.0 29.0

Mean 12022 83.5 28.8

IN Tippecanoe C Moderate Y L 4400 6900 7532 53.5 28.2

IN Tippecanoe C Moderate Y CL 4900 7700 8429 59.5 28.3

Mean 7981 56.5 28.3

Table 1. Increases in SOC due to switching from intensive-till to no-till or moderate-till in counties selected to

 represent the range and mean of county-level responses in Indiana, Smith et al. (2002). 

Quadratic SOC Response

?SOC
Estimating State-Level SOC Responses

F-5

IN Gibson C/SB No N L 4300 6700 7253 52.5 27.6

IN Gibson C/SB No N SICL 3600 5700 6308 43.5 29.0

Mean 6781 48.0 28.3

IN Gibson C/SB No Y L 3500 5400 5778 43.0 26.9

IN Gibson C/SB No Y SICL 2600 4000 4267 32.0 26.7

Mean 5023 37.5 26.8

IN Gibson C/SB Moderate N L 2900 4600 5104 35.0 29.2

IN Gibson C/SB Moderate N SICL 2900 4700 5410 34.5 31.4

Mean 5257 34.8 30.3

IN Gibson C/SB Moderate Y L 2300 3500 3692 28.5 25.9

IN Gibson C/SB Moderate Y SICL 1900 3100 3616 22.5 32.1

Mean 3654 25.5 29.0

IN Gibson C No N L 8700 13400 14311 107.0 26.8

IN Gibson C No N SICL 7300 11900 13856 86.5 32.0

Mean 14084 96.8 29.4

IN Gibson C No Y L 6400 9900 10625 78.5 27.1

IN Gibson C No Y SICL 5000 7000 7042 65.0 21.7

Mean 8834 71.8 24.4

IN Gibson C Moderate N L 5900 9100 9734 72.5 26.9

IN Gibson C Moderate N SICL 6100 10700?

Mean 9734 72.5 26.9

IN Gibson C Moderate Y L 3800 6000 6613 46.0 28.8

IN Gibson C Moderate Y SICL 4000 6500 7521 47.5 31.7

Mean 7067 46.8 30.3

E Years to

State County Rotation Tillage Hydric Texture 10 years 20 years ?SOCs (slope factor) ?SOCs (Ys)

kg C/ha

IN Tippeconoe C/SB No N Mean 8289 59.0 28.1

IN Gibson C/SB No N Mean 6781 48.0 28.3

Interpolated state curve 4400 6901 7535 53.5 28.2

State mean--Smith et al. (2002) 4225 not avail.

IN Tippeconoe C/SB No Y Mean 7059 48.3 29.3

IN Gibson C/SB No Y Mean 5023 37.5 26.8

Interpolated state curve 3527 5532 6041 42.9 28.0

State mean--Smith et al. (2002) 3500 not avail.

IN Tippeconoe C/SB Moderate N Mean 5672 43.3 28.8

IN Gibson C/SB Moderate N Mean 5257 34.8 30.3

Interpolated state curve 3204 5016 5464 39.0 29.6

State mean--Smith et al. (2002) 3200 not avail.

IN Tippeconoe C/SB Moderate Y Mean 4678 33.5 27.9

IN Gibson C/SB Moderate Y Mean 3654 25.5 29.0

Interpolated state curve 2428 3811 4166 29.5 28.5

State mean--Smith et al. (2002) 2400 not avail.

IN Tippeconoe C No N Mean 15612 93.5 33.4

IN Gibson C No N Mean 14084 96.8 29.4

Interpolated state curve 7989 12931 14848 95.1 31.4

IN Tippeconoe C No Y Mean 10000 76.8 26.1

IN Gibson C No Y Mean 8834 71.8 24.4

Interpolated state curve 5961 8995 9417 74.3 25.2

IN Tippeconoe C Moderate N Mean 12022 83.5 28.8

IN Gibson C Moderate N Mean 9734 72.5 26.9

Interpolated state curve 6402 10007 10878 78.0 27.9

IN Tippeconoe C Moderate Y Mean 7981 56.5 28.3

IN Gibson C Moderate Y Mean 7067 46.8 30.3

Interpolated state curve 4277 6783 7524 51.6 29.3

Quadratic SOC Response

?SOC

Interpolated state curves--means of Tippaconoe and Gibson County curves
Estimating State-Level SOC Responses

F-6

IN State C/SB No Mean Mean 4217 6614 7220.8 51.3 28.2

IN State C/SB Moderate Mean Mean 3041 4763 5191.6 37.0 28.1

IN State C No Mean Mean 7587 11942 13103.5 92.0 28.5

IN State C Moderate Mean Mean 5956 9331 10173.6 72.5 28.1

Weighted state means assuming 79% non-hydric soils and 21% hydric soils 
Estimating State-Level SOC Responses

F-7

Table F-2

Increases in SOC due to switching from intensive-till to no-till or moderate-till in Iowa

E Years to

State County Rotation Tillage Hydric Texture 10 years 20 years ?SOCs (slope factor) ?SOCs (Ys)

kg C/ha

IA Buchanan C/SB No N L 5800 9200 10208 70.0 29.2

IA Buchanan C/SB No N CL 5800 9200 10208 70.0 29.2

Mean 10208 70.0 29.2

IA Buchanan C/SB No Y L

IA Buchanan C/SB No Y CL 6400 9900 10625 78.5 27.1

Mean 10625 78.5 27.1

IA Buchanan C/SB Moderate N L 3800 6100 6901 45.5 30.3

IA Buchanan C/SB Moderate N CL 4000 6400 7200 48.0 30.0

Mean 7051 46.8 30.2

IA Buchanan C/SB Moderate Y L

IA Buchanan C/SB Moderate Y CL 4200 6400 6760 52.0 26.0

Mean 6760 52.0 26.0

IA Buchanan C No N L 7600 12100 13504 91.5 29.5

IA Buchanan C No N CL 7500 11900 13210 90.5 29.2

Mean 13357 91.0 29.4

IA Buchanan C No Y L

IA Buchanan C No Y CL 8300 13100 14429 100.5 28.7

Mean 14429 100.5 28.7

IA Buchanan C Moderate N L 3100 5000 5704 37.0 30.8

IA Buchanan C Moderate N CL 3500 5700 6624 41.5 31.9

Mean 6164 39.3 31.4

IA Buchanan C Moderate Y L

IA Buchanan C Moderate Y CL 3700 6000 6914 44.0 31.4

Mean 6914 44.0 31.4

IA Greene C/SB No N L 5200 8200 9020 63.0 28.6

IA Greene C/SB No N CL 5300 8500 9601 63.5 30.2

Mean 9311 63.3 29.4

IA Greene C/SB No Y L 5400 8500 9327 65.5 28.5

IA Greene C/SB No Y CL 5500 8700 9614 66.5 28.9

Mean 9471 66.0 28.7

IA Greene C/SB Moderate N L 3400 5300 5741 41.5 27.7

IA Greene C/SB Moderate N CL 3600 5700 6308 43.5 29.0

Mean 6025 42.5 28.4

IA Greene C/SB Moderate Y L 3500 5500 6021 42.5 28.3

IA Greene C/SB Moderate Y CL 3700 5900 6601 44.5 29.7

Mean 6311 43.5 29.0

IA Greene C No N L 7300 11500 12633 88.5 28.5

IA Greene C No N CL 7300 11700 13200 87.5 30.2

Mean 12917 88.0 29.4

IA Greene C No Y L 7600 11900 12964 92.5 28.0

IA Greene C No Y CL 7500 11900 13210 90.5 29.2

Mean 13087 91.5 28.6

IA Greene C Moderate N L 2500 4000 4500 30.0 28.6

IA Greene C Moderate N CL 2500 4000 4500 30.0 28.6

Mean 4500 30.0 28.6

IA Greene C Moderate Y L 2800 4400 4817 34.0 28.3

IA Greene C Moderate Y CL 2900 4700 5410 34.5 34.1

Mean 5114 34.3 31.2

Table 2. Increases in SOC due to switching from intensive-till to no-till or moderate-till in counties selected to represent

 the range and mean of county-level responses in Iowa, Brenner et al. (2001). 

Quadratic SOC Response

?SOC
Estimating State-Level SOC Responses

F-8

IA Pottawattamoe C/SB No N L 5700 9000 9919 69.0 28.8

IA Pottawattamoe C/SB No N CL 5500 8600 9352 67.0 27.9

Mean 9636 68.0 28.4

IA Pottawattamoe C/SB No Y L

IA Pottawattamoe C/SB No Y CL

Mean

IA Pottawattamoe C/SB Moderate N L 3700 5800 6328 45.0 28.1

IA Pottawattamoe C/SB Moderate N CL 3800 6000 6613 46.0 28.8

Mean 6471 45.5 28.5

IA Pottawattamoe C/SB Moderate Y L

IA Pottawattamoe C/SB Moderate Y CL

Mean

IA Pottawattamoe C No N L 7900 12600 14102 95.0 29.7

IA Pottawattamoe C No N CL 7700 11900 12758 94.5 27.0

Mean 13430 94.8 28.4

IA Pottawattamoe C No Y L

IA Pottawattamoe C No Y CL

Mean

IA Pottawattamoe C Moderate N L 2900 4900 6235 33.5 37.2

IA Pottawattamoe C Moderate N CL 2800 4600 5445 33.0 33.0

Mean 5840 33.3 35.1

IA Pottawattamoe C Moderate Y L

IA Pottawattamoe C Moderate Y CL

Mean

E Years to

State County Rotation Tillage Hydric Texture 10 years 20 years ?SOCs (slope factor) ?SOCs (Ys)

kg C/ha

IA Buchanan C/SB No N Mean 10208 70.0 29.2

IA Greene C/SB No N Mean 9311 63.3 29.4

IA Pottawattamoe C/SB No N Mean 9636 68.0 28.4

Interpolated state curve 5551 8786 9718 67.1 29.0

State mean--Brenner et al. (2001) 5600 not avail.

IA Buchanan C/SB No Y Mean 10625 78.5 27.1

IA Greene C/SB No Y Mean 9471 66.0 28.7

IA Pottawattamoe C/SB No Y Mean

Interpolated state curve 5926 9255 10048 72.3 27.9

State mean--Brenner et al. (2001) 5800 not avail.

IA Buchanan C/SB Moderate N Mean 7051 46.8 30.2

IA Greene C/SB Moderate N Mean 6025 42.5 28.6

IA Pottawattamoe C/SB Moderate N Mean 6471 45.5 28.5

Interpolated state curve 3718 5887 6515 44.9 29.1

State mean--Brenner et al. (2001) 3700 not avail.

IA Buchanan C/SB Moderate Y Mean 6760 52.0 26.0

IA Greene C/SB Moderate Y Mean 6311 43.5 29.0

IA Pottawattamoe C/SB Moderate Y Mean

Interpolated state curve 3903 6061 6536 47.8 27.5

State mean--Brenner et al. (2001) 3850 not avail.

IA Buchanan C No N Mean 13357 91.0 29.4

IA Greene C No N Mean 12917 88.0 29.4

IA Pottawattamoe C No N Mean 13430 94.8 28.4

Interpolated state curve 7552 11958 13235 91.3 29.0

IA Buchanan C No Y Mean 14429 100.5 28.7

IA Greene C No Y Mean 13087 91.5 28.6

IA Pottawattamoe C No Y Mean

Interpolated state curve 7925 12501 13758 96.0 28.7

IA Buchanan C Moderate N Mean 6164 39.3 31.4

IA Greene C Moderate N Mean 4500 30.0 28.6

IA Pottawattamoe C Moderate N Mean 5840 33.3 35.1

Interpolated state curve 2886 4711 5501 34.2 31.7

IA Buchanan C Moderate Y Mean 6914 44.0 31.4

IA Greene C Moderate Y Mean 5114 34.3 31.2

IA Pottawattamoe C Moderate Y Mean

?SOC

Quadratic SOC Response

Interpolated state curves--means of Buchanan, Tippaconoe, and Pottawattamoe County curves
Estimating State-Level SOC Responses

F-9

IA State C/SB No Mean Mean 5637 8895 9793.8 68.3 28.7

IA State C/SB Moderate Mean Mean 3761 5929 6519.8 45.6 28.6

IA State C No Mean Mean 7638 12083 13354.9 92.3 28.9

IA State C Moderate Mean Mean 2976 4843 5619.2 35.3 31.8

Weighted state means assuming 77% non-hydric soils and 23% hydric soils 
Estimating State-Level SOC Responses

F-10

References

1 Smith, P., J. Brenner, K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, K. Killian, 

D. Lamm, J. Schuler, and S. Williams. 2002. Quantifying the change in greenhouse gas 

emissions due to natural resource conservation practice application in Indiana. Final report 

to the Indiana Conservation Partnership. Colorado State University Natural Resource 

Ecology Laboratory and USDA Natural Resources Conservation Service, Fort Collins, 

Colorado, USA.

2 Brenner, J., K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, T. Kautza, K. Killian, 

J. Schuler, and S. Williams. 2001. Quantifying the change in greenhouse gas emissions due 

to natural resource conservation practice application in Iowa. Final report to the Iowa 

Conservation Partnership. Colorado State University Natural Resource Ecology Laboratory 

and USDA Natural Resources Conservation Service, Fort Collins, Colorado, USA.

3 Bock, B. R., F. J. Sikora, and G. W. Hergert. 1991. Approach for estimating yield response 

to nitrogen based on nitrogen-rate requirement parameters. p. 323-332. In: R.F. Follett and 

D.R. Keeney (eds.). Managing nitrogen for groundwater quality and farm profitability. 

ASA, Madison, Wisconsin.