# Economic Evaluation of CO 2 

# 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 Infr astructure 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 Pub lic Power Institute

### ii DISCLAIMER

This report was prepared as an account of wor k 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, comp leteness, 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, man

ufacturer, 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 tho se 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 CO 2 

### storage and sink enhancement. The technologies evaluated included options for s toring captured CO 2 

### 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 MW e 

### integrated gasificatio n 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 CO 2 

### mitigation mandates, ener gy companies need better cost information on CO 2 

### mitigation options, especially storage and sink enhancement options that involve non

- energy company operations. One of the major difficulties in evaluating CO 2 

### storage and sink enhancement options is obtaini ng consistent, transparent, accurate, and comparable economics. Objective To develop consistent, transparent, accurate, and comparable economics of direct and indirect sequestration of CO 2 

### 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 CO 2 

### 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 CO 2 

### 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) emissi ons avoided by the capture and storage options assume CO 2 

### capture and injection into storage reservoirs during the 20

- year book life of the capture and storage plants and storage of injected CO 2 

### for another 80 years.

### iv

Results Costs for the base cases rang ed from $

- 43 to 39/tonne of CO 2 

### 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 reservoi r 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 CO 2 

### capture and storage are gen erally 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. Sit e

- 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 develop ed 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 tha nk 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 tech nical input and guidance.
We would like to thank EPRI (especially Richar d 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 investig ators listed on the cover page, we would like to thank Gemma Heddle of MIT for her extensive and effec tive involvement in assessment of the CO 2 

### storage options, J. David Wallace of TVA for his outstan ding 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 CO 2 

### mitigation mandates, energy companies need better information on the costs of alternative CO 2 

### mitigation options, especially storage and sink enhan cement options that involve non

- energy company operations. One of the major difficulties in evaluating CO 2 

### 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 CO 2 

### storage and sink enhancement, including options for storing captured CO 2 

### , such as active oil reservoirs, depleted oil and gas reservoirs, deep aquifers, coal beds, and oceans, as well as the enh ancement 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
e fforts to select specific sequestration processes to be evaluated for captured CO 2 

### 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 CO 2 

### storage processes and helped TVA develop economic models for comparing technologies and practices. The University of Tennessee Agricultural Policy Research Center, in co llaboration with TVA, evaluated the economics of enhancing CO 2 

### 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 CO 2 

### capture, with the assumption that CO 2 

### sequestration is a small part of these costs. In addition, these comparisons have used information supplied from studies of specific te chnologies, 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 do ne.

### 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 sign ificantly 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 pr oject 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 CO 2 

### capture being developed for DOE. Pr obability 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 we re 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 CO 2 

### storage options were size d to accommodate the CO 2 

### from our base case capture plant (see the design basis in the CO 2 

### capture, transport and storage section). In addition, the amount of forest and cropland required to sequester the same amount of CO 2 

### over a 100

- year planning horizo n 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 decisio n 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 CO 2 

### and sink enhancement, it was felt that at least one of each type sh ould 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
For ests 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) CO 2 

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

### Capture, Transport and Storage

Ove rview This section summarizes the basis of design, methodology, and results for calculating the costs for transport and storage of captured CO 2 

### . Additional details and background information are contained in Chapters 3

- 9. At the end of this section, the c apture 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

• Enhan ced 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 conce ptual 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 MW e 

### gross integrated gasification combined cycle (IGCC) plant operating at an 80 percent capacity factor was utilized as the production source of CO 2 

### . This was based on the DOE/EPRI’s recent study on the “Evaluation of Innovative Fossil Fuel Power Plants with CO 2 

### 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 CO 2 

### Removal”.

1 “Evaluation of Innovative Fossil Fuel Power Plants with CO 2 Removal,” EPRI Report No. 1000316, Int erim 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 CO 2 removal

Parameter Unit V alue Thermal Input, HHV 10 6 Btu/h 3,723 Gross Power Output MW 490.4 Net Power Output MW 403.5 Efficiency, HHV % 37.0 Capacity Factor % 80
CO 2 Captured t/d 7,389
million scm/d 3.76
CO 2 Emitted kg/kWh 0.073
CO 2 Avoided in Capture t/d 6,246
CO 2 Cap ture Cost $/t captured 14.55
CO 2 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
10 6 Btu = 1.06 GJ


### In the case of the ocean storage options, it is assumed that three IGCC power plants supply CO 2 

### to a shoreline collectio n 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 CO 2 

### , i.e. 11.29 million scm (22,167 tonnes), as opposed to 3.76 millio n scm (7, 389 tonnes), of CO 2 

### per day.

### xi

A pipeline specification of (152 bar,

- 40oC dewpoint, N 2 

### <300 ppmv, O 2 

### <40 ppmv, Ar<10 ppmv) was used for the quality of the CO 2 

### . The DOE/EPRI study is consistent with this except for the CO 2 

### pressure. The DOE/EPRI stu dy was based on compressing the CO 2 

### to 83 bar. This study includes additional compression to 152 bar.
Methodology Pipeline Transport 

### The pipeline inlet CO 2 

### pressure is set equal to 152 bar, which is equivalent to the pressure of the CO 2 

### supplied by the ba se case IGCC plant. Based on a recommendation that the pipeline CO 2 

### pressure not be allowed to fall below 103 bar, this latter value is used for the pipeline outlet CO 2 

### pressure. The maximum allowable pressure drop per unit length ( ?P/?L) is found as the difference between the pipeline inlet and outlet CO 2 

### pressures divided by the pipeline length. The pipeline diameter is then calculated using the equations for pressur e 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 CO 2 

### pipelines. The cost data found for natural gas pipelines consists o f 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 CO 2 

### 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. Figu re ES

- 1 shows the cost per tonne of CO 2 

### per 100 km as a function of CO 2 

### mass flow rate. Economies of scale are reached with annual CO 2 

### flow rates in excess of 10 million tonnes per year. At these rates, transport costs are less than $1 per tonne of CO 2 

### p er 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 CO 2 

### /yr)Cost ($/t CO2/100km)

Figure ES

- 1 Cost for CO

2 transport via pipeline as a function of CO 2 mass flow rate



### Note that these costs are not on a CO 2 

### 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 CO 2 

### and reservoir oil would be modeled to assess the likely performance of a proposed CO 2 

### - EOR project. Based on the output of this modeling, the cost of the proposed CO 2 

### flood is calculated. However, for our purposes of developing general costing algorithms, ‘rules of thumb’ are used to define the engineering parameters nee ded to estimate the cost of a CO 2 

### - 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 illustratio n 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 CO 2 

### mass flow rate is determined using a CO 2 

### effectiveness factor of 170 standard cubic meters (6,000 standard cubi c feet) of new CO 2 

### 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 CO 2 

### recycle plant is determined based on a maximum CO 2 

### 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 

### repo rt 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, comp rises 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 CO 2 

### storage options assessed in this project were calculated on a CO 2 

### 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 CO 2 Effectiveness scm/bbl enhanced oil 170 227 85 Oil Production per We ll 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 CO 2 * scm/day/module 68,000 45,000 59,000 Maximum Recycled CO 2 * scm/day/module 204,000 182,000 59,000

Levelized Annual CO 2 Net Storage Cost* $/tonne CO 2 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 O il 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 CO 2 

### - EOR concept design, ‘rules of thumb’ are used to define the engineering parameters needed to estimate the cost of a CO 2 

### - 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 CO 2 

### mass flow rate is determined using a CO 2 

### effectiveness factor of two scm CO 2 

### 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 fr om the number of production wells. Fourth, it is assumed that no recycling of CO 2 

### is required. Finally, the cost of drilling and equipping the required production and injection wells is calculated.
Prior to acquiring a lease position, geological expendit ures, 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 ‘ Cost s and Indices for Domestic Oil and Gas Fi eld Equipment and Production Operations’
r eport. A representative ECBMR lease, or module, comprising 10 CO 2 

### injection wells and 10 producing wells with dewatering facilities is used for the design basis. The 10 CO 2 

### 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 wel l 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 rel ationship, 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 = 0.0888e 0.0008x



### 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 descri ptions and costing results

Parameter Units ECBMR Base Case ECBMR

High Cost Case ECBMR

Low Cost Case CO 2 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 $/10 6 Btu 2 1.80 3 De pth 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 CO 2 Wells* 135 126 84 New CO 2 * scm/d/well 28,000 30,000 45,000 Levelized Annual CO 2 Net Storage Cost* $/tonne CO 2 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 CO 2 

### 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 CO 2 

### storage options.
The cost model for the geologic CO 2 

### 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 CO 2 

### flow rate, CO 2 

### downhole injection pressure, and set of reservoir parameters. Second, an iterative procedure is used to take into account the interdependent relationship between CO 2 

### downhole inje ction 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 CO 2 

### mass flow rate, CO 2 

### downhole injection pressure, and reservoir pressure, thic kness, depth, and permeability. The relationship shown in Figure ES

- 3 is used to determine CO 2 

### injectivity from CO 2 

### mobility 4 

### . CO 2 

### injectivity is defined as the mass flow rate of CO 2 

### (m) that can be injected per unit of reservoir thickness (h) and per
u nit of downhole pressure difference (P inj


### –P res 

### ). CO 2 

### mobility is defined as the CO 2 

### absolute permeability divided by the CO 2 

### viscosity. Given the CO 2 

### injectivity, the CO 2 

### injection rate per well (Q) can be calculated as
Q = CO 2 

### injectivity x h x ( P inj


### –P res 

### )
Finally, the number of wells required (n) is given by
n = m/Q

4 Law, D. and S. Bachu, “Hydrogeological and numerical analysis of CO 2 disposal in dee p aquifers in the Alberta sedimentary basin,” Energy Convers. Mgmt. , 37:6

- 8, pp. 1167

- 1174, 1996.

### xvii CO 2 

### Injectivity as a Function of CO 2 

### Mobility

y = 0.0208x

0.10 1.00 10.00 100.00

10 100 1000 10000 CO 2 

### mobility (md/mPa.s)CO2injectivity (t/d/m/MPa)

Figure ES

- 3 CO 2 injectivity as a function of CO 2 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 ‘Co sts 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 o n 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 containe d 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 res ervoirs, 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
Thic kness 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 CO 2 Storage Cost* $/tonne CO 2 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 2,13 4 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 CO 2 Storage Cost* $/tonne CO 2 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 CO 2 Storage Cost* $/tonne CO 2 eq. LC GHG avoided 2.93 11.71 1.14

* calculated


### Ocean Via Pipeline 

### The ocean pipeline storage option assumes that three I GCC power plants supply CO 2 

### to a shoreline collection point. The CO 2 

### is then transported via a subsea pipeline from the shoreline to a depth of 2,000 m, at which depth the CO 2 

### is discharged into the deep ocean via a diffuser unit. The method used for cal culating 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 CO 2 

### .

### xx

The pipelin e diameter is calculated using the same method as is used in the CO 2 

### 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 CO 2 

### overland pipel ine transport, the pressure drop per unit length is simply found as the difference between the pipeline CO 2 

### inlet and outlet pressures divided by the pipeline length. The pipeline ocean CO 2 

### storage model however requires that the pressure drop per unit le ngth calculation also take into account the gravity head gain and diffuser head loss. In addition, it is necessary that the CO 2 

### be discharged at a pressure equal to the hydrostatic pressure.
The cost of the subsea pipeline has been determined based on cos t information contained in McDermott’s phase II final report on ‘Large

- scale CO 2 

### 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 k m 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 CO 2 Storage Cost* $/tonne CO 2 eq. LC GHG avoided 5.53 14.23 2.90

* calculated, 1 inch = 0.0254 m



### Ocea n Via Tanker 

### The storage option has been modeled based on design and cost information obtained from McDermott’s Phase I and Phase II 8 

### final reports on ‘Large

- scale CO 2 

### Transportation and Deep Ocean Sequestration’ as well as e

- mail communications with the r eports’ author. The method

6 Sarv, H., “Large

- scale CO 2 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 CO 2 transportation and deep ocean sequestration,” McDermott Technology Inc., Ohio. Tech. Rep. DE

- AC 26

- 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 CO 2 

### to the offshore platform is determined. Second, the diameter of the vertical pi pe to carry the CO 2 

### from the platform to the injection depth is calculated. Third, the amount of CO 2 

### 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 CO 2 

### . The total capital cost of the tanker ocean CO 2 

### storage option comprises the c apital 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 wi th 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 comprise d 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 determine d 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 Platf orm $ 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_capi tal_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_pipeli ne_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 Pip eline 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

CO 2 Emitted by Tankers* t/y 2,395 7,186 1,198

CO 2 Emi tted by Boil Off* t/y 53,362 139,415 24,638 Levelized Annual CO 2 Storage Cost* $/tonne CO 2 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 vari ous 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 t anker option, the typical base case costs for CO 2 

### storage (transport + injection) without oil or gas by

- product credit is in the range of $3

- 5.50 per tonne CO 2 

### ($11

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

- 15 per tonne CO 2 

### ($7

- 55 per ton ne 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 CO 2 

### and still breakeven (i.e., the costs equal the by

- product cred it).

• 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 CO 2 

### stored to the oil or gas produced. With more oil or gas produc ed per unit of CO 2 

### stored, the lower net CO 2 

### storage cost, but the less CO 2 

### 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 CO 2 

### 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 CO2eq. 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 CO2eq. LC GHG avoided

Figure ES

- 5 Levelized annual cost comparison of carbon sequestration technologies

### xxv

Table ES

- 12 CO 2 Capture + Net Storage Costs: Base Cases, NPV Basis

CO 2 Storage Process LC GHG avoided $/tonne CO 2 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



### CO 2 

### Sink Enhancement via Improved Forest Management
Basis and Approach Costs of the CO 2 

### 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 CO 2 

### to serve as CO 2 

### 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 CO 2 

### sink enhancement, (2) changes in GHG emissions other than from CO 2 

### 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 CO 2 

### 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 inv olve CO 2 

### capture and storage or CO 2 

### 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 CO 2 

### 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 d eforestation. 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
assesse d, 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 Pin e - 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 above

ground 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

- CO 2 

### 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 CO 2 

### storage options with reven ues (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 ago

forestry 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

- CO 2 

### 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 PineOak Miombo 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 CO 2 

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

Table ES

- 14 Forestry costs for three productivity levels and CO 2 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 CO 2 $/tonne CO 2 $/tonne CO 2 $/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

CO 2 

### 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 N 2 

### O emissions from the soil; these factors combined with the added carbon sequestered in soil organic matter determine the life

- cycle (LC) GHG emissions avoid ed by reducing tillage. Effects of reducing tillage on equipment, fuel, herbicide, and nitrogen (N) fertilizer use and on N 2 

### O emissions continue as long as the switch to reduced

- tillage continues. Costs to an energy company are an adoption incentive to g et 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 equivale nt (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 CO 2 

### 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 invo lve CO 2 

### capture and storage or CO 2 

### 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 CO 2 

### 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 i n 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 N 2 

### O 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 N 2 

### O 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 N 2 

### O emissions due to reducing tillage.

### xxix

4

. Chapter 22: Intensive

- til l cotton to no

- till cotton in the southeastern U.S. This case includes variations in the amount of soil SOC sequestered and amount of N 2 

### O emissions due to reducing tillage. Summary of Costs
Base

- case costs of avoided CO 2 

### 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 CO 2 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 CO 2 equivalent LC GHG avoided



### Inputs used to estimate the base

- case costs of CO 2 

### 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 CO 2 

### 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 i n 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 mainl y because corn is less well adapted to no

- till than are soybeans.

Costs of CO 2 

### 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 sequester s less additional carbon in soil organic matter but also requires a smaller adoption incentive and results in a decrease rather than in increase in N 2 

### O emissions.

### xxx Table ES

- 15

Base
- case inputs and costs of CO 2 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 Δ SOCs, 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 N 2 O (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 N 2 O 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 CO 2 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, switchi ng to no

- till wheat/fallow reduces N 2 

### O emissions whereas switching to no

- till corn/soybeans increases N 2 

### O emissions.

Costs of CO 2 

### abatement are significantly higher for cotton than for corn/soybean rotations, mainly because switching to no

- till increases N 2 

### O emissions significantly more with cotton than with corn/soybean rotations. The greater increase in N 2 

### O 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 N 2 

### O 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 N 2 

### O has a radiative forcing factor 296 times greater the CO 2 

### on a mass basis. Effects of increases in N fertilizer rate and N 2 

### O emissions on

LC GHG avoided and costs of GHG avoidance are discussed below.
Examples of the large effects of N rate and N 2 

### O 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 s witching from intensive

- till to no

- till in a corn/soybean rotation in the Corn Belt. No significant change in N 2 

### O emissions due switching from intensive

- till to no

- till is a possibility with good N management, especially on well

- drained soils. The increa ses in N rate and N 2 

### O 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 CO 2 

### equivalent LC GHG avoided/ha) with a be st

- case N rate and N 2 

### O scenario to

- 3.0 tonnes CE LC GHG avoided/ha (

- 11.0 tonnes CO 2 

### 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 CO 2 

### equivalent LC GHG avoided) with a be st

- case N rate and N 2 

### O scenario to $113/tonne CE LC GHG avoided ($31/tonne CO 2 

### 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 annu al 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 fertil izer used and the N 2 

### O 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 N 2 O emissions on amounts and costs of avoided GHG via switching from i ntensive
- till to no
- till in a Corn/Soybean rotation

? N rate ? N 2 O 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 avoided 1 /

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 N 2 O 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 CO 2 

### capture, transport and storage and indirec t 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 CO 2 

### 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 CO2Eq. 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 CO2Eq. 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 CO 2 

### 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 CO 2 

### capture and injection into storage reservoirs during the 20

- year book life of the capture an d storage plants and storage of injected CO 2 

### for another 80 years. For the ocean storage options, the CE LC GHG avoided is reduced for leakage of CO 2 

### throughout the 100

- year storage period. Zero CO 2 

### leakage is assumed for the other CO 2 

### 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 CO 2 

### storage options.

### xxxv

Table ES

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

- case CO 2 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 CO 2 Capture & Storage CO 2 Capture & Storage CO 2 Capture & Storage CO 2 Capture & Storage CO 2 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. Th e 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 st orage. 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 info rmation 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 th e 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 upd ates 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 Enhancemen t

................................ ................................ .................. 1

- 15 1.3 Report Organization

................................ ................................ ................................ ........ 1

- 17 1.4 References

................................ ................................ ................................ ...................... 1

- 17

2 METHODOLOGIES FOR C ALCULATING THE COST OF CAPTURING AND STORING CO 2 

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

................................ ................................ ................ 2

- 1 2.1.1 Approach for CO 2 

### Capture

................................ ................................ ........................ 2

- 1 2.1.2 Adjustment for CO 2 

### Storage

................................ ................................ ...................... 2

- 3 2.1.3 Adjustme nt of Levelized Carrying Charge Factor as a Function of Discount Rate

................................ ................................ ................................ ................................ .... 2

- 3 2.1.4 Impact of CO 2 

### Storage Costs on Cost of Electricity

................................ ................. 2

- 4 2.2 Capture Costs on CO 2 

### Captured, CO 2 

### Avoided, and Life

- Cycle GHG Avoided Bases

................................ ................................ ................................ ................................ ....... 2

- 5 2.2.1 CO 2 

### Captured Basis

................................ ................................ ................................ .. 2

- 5 2.2.2 C O 2 

### 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 CO 2 

### Captured Basis

................................ ................................ ................................ .. 2

- 7 2.3.2 CO 2 

### Avoided Basis (CO 2 

### Capture and Storage Combined)

................................ ..... 2

- 8

### xxxviii

2.3.2 CO 2 

### Avoided Basis (CO 2 

### Capture and Storage Combined)

................................ ..... 2

- 9 2.3.3 GHG Avoided in CO 2 

### Capture and Storage Processes Combined on a Life

- Cycle (LC) Basis

................................ ................................ ................................ .......... 2

- 9 2.3.4 Costs of CO 2 

### Storage on a Life

- Cycle GHG Avoided Basis (Storage Only)

.......... 2

- 10 2.4 Comparing the Economics of CO 2 

### Storage and Sink Enhancement Options Differing in Timing and Permanence of Sequestration

................................ ......................... 2

- 11 2.5 References

................................ ................................ ................................ ...................... 2

- 14

3 STORING CAPTURED CO 2 

### – 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 M ining

................................ ................................ .................... 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 CO 2 

### ................................ ................................ . 3

- 7 3.4.6 Emissions fro m the Transportation of CO 2 

### ................................ ............................... 3

- 7 3.4.7 Emissions from the CO 2 

### 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 Mode l and Studies

................................ ..................... 4

- 12

### xxxix

4.8 References

................................ ................................ ................................ ...................... 4

- 15

5 ENHANCED OIL RECOVE RY

................................ ................................ ................................ . 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 CO 2 

### 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 CO 2 

### Recycle Ratio

................................ ................................ ................................ .. 5

- 11 5.4.5 Reworking of Existing Wells

................................ ................................ .................... 5

- 11 5.4.6 Cost Calculations

................................ ................................ ................................ .... 5

- 12 5.5 D esign 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 ME THANE

................................ ................................ ....................... 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 CO 2 

### 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 CO 2 

### 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 OI L 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 CO 2 

### Downhole Injec tion 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: Spr eadsheet 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 Descripti on

................................ ................................ ................................ .......... 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 CO 2 

### 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 CO 2 

### 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 S ink Enhancement

................................ ................................ ................... 10

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

.................... 10

- 2 10.2.4 Non

- CO 2 

### 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

- CO 2 

### 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 Re ferences

................................ ................................ ................................ .................. 10

- 14

11 CASE 1: SOUTHERN U. S. LOBLOLLY PINE PLA NTATIONS

................................ ......... 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 Cla ssification 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 NOR THWEST DOUGLAS FIR P LANTATIONS

................................ .. 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 Mai ntenance

................................ ................................ ........... 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 CED AR PLANTATIONS IN ME XICO USING THE TAUNG YA SYSTEM

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

................................ ................................ ................................ .................... 13

- 1 13.2 Ecology

................................ ................................ ................................ .......................... 13

- 1 13.3 Manage ment 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 Stora ge 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: RESTORATIO N OF DEGRADED PINE
- OAK WOODLAND IN CHIAPAS, MEXICO THROUGH ENRIC HMENT PLANTING WITH PINUS OOCARPA

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

................................ ................................ ................................ .................... 14

- 1 14.2 Ecology 1,2,3,4



### ................................ ................................ ................................ ................... 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 MIO MBO 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 Esta blishment 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 ORCHARD S IN SEMI

ARID 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 / Informa tion Sources

................................ ................................ ................ 16

- 8

17 CASE 7: AVOIDANCE O F DEFORESTATION IN C HIAPAS, 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 Potent ial Income

................................ ................................ ................................ .... 17

- 8 17.5 Summary of Model Inputs and Results

................................ ................................ ......... 17

- 8 17.6 References

................................ ................................ ................................ .................. 17

- 11

18 ECONOMICS OF CO 2 

### SINK ENHANCEMENT VI A REDUCING TILLAGE O N 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 N 2 

### O 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 ROTATI ONS 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 N

2 

### O 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 (Po or 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 F uel 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 N

2 

### O 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 Sensit ivity 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 PLAIN S

................................ .................... 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 N

2 

### O 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 N 2 

### O Emissions)

............. 21

- 10 21.3 Wheat/Fallow (Sensitivity Cases with No Tillage Effect on N 2 

### O Emissions)

............. 21

- 11 21.3.1 Summary of Avoided GHG Emissions

................................ ................................ 2 1

- 11 21.3.2 Summary

................................ ................................ ................................ ............. 21

- 12 21.3.3 Sensitivity Summary

................................ ................................ ............................ 21

- 12 21.4 References

................................ ................................ ................................ .................. 21

- 15

22 COTTON IN THE SOUTH EASTERN UNITED STATE S

................................ .................... 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 N

2 

### O 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 N 2 

### O Emissions)

........................ 22

- 10 22.3 Cotton (Sensitivity Cases with No Tillage Effect on N 2 

### O 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 CAPTURIN G AND STORING CO 2 

### ................................ ................................ ................................ ............................. A
- 1

B EXPLANATION OF PROC ESS INPUT AND SUMMAR Y 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 CO 2 

### Storage Costs (years 1

- 20)

............. B

- 2 Summary Sheet Section 2

- Levelized Annual CO 2 

### Capture and Net Storage Costs (years 1

- 20)

................................ ................................ ................................ ........................ B

- 2 Summary Sheet Section 3

- CO 2 

### 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 EXISTI NG FORESTRY SEQUESTR ATION PROJECTS

.......... C

- 1 References

................................ ................................ ................................ .............................. C

- 4

### li

D PUBLISHED COSTS OF FORESTRY CARBON SEQU ESTRATION

................................ .. D
- 1 References

................................ ................................ ................................ .............................. D

- 2

E ESTIMATING ECONOMIC INCENTIVES REQUIRED TO ACHIEVE ADOPTION OF CONSERVATION

-

TILLAGE SYSTEMS IN S ELECTED REGIONS OF T HE UNITED STA TES

................................ ................................ ................................ ................................ ....... 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 Referenc es 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 CO 2 

### produced, captured, and avoided when comparing power plants with and without CO 2 

### capture (based on Table 2-2) .....................................2-6 Figure 2-3 Relationships among GHG produced, CO 2 

### captured, and GHG avoided on a LC basis for CO 2 

### 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 CO 2 

### 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 CO 2 

### for the base case as a function of CO 2 

### 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 CO 2 

### injectivity as a function of CO 2 

### 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 CO 2 

### 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 CO 2 

### 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 CO 2 

### 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 conservati on-tillage systems affect the amount of carbon that remains stored in soil organic matter ............................................................18-3 Figure 18-2 Increases in soil organic carbon (S OC) 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 CO 2 

### transport and storage cost comparison on a levelized annual $/t of CO 2 

### equivalent life cycle greenhouse gas avoided basis ........................................................23-2

### lv Figure 23-2 CO 2 

### capture, transport and storage cost comparison on a NPV $/t of CO 2 

### equivalent life cycle greenhouse gas avoided basis ........................................................23-3 Figure 23-3 Forestry cost comparison on a NPV $/t of CO 2 

### equivalent life cycle greenhouse gas avoided basis ........................................................................................23-5 Figure 23-4 Cropland cases NPV $/t CO 2 

### 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 Eco nomic 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 ca lculating the levelized carrying charge factor

................. 2

- 2 Table 2

- 2 Calculation of CO 2 

### capture costs on CO 2 

### captured and CO 2 

### 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 CO 2 

### storage operations

................................ ...... 2

- 11 Table 3

- 1 Summary of parameters for IGCC power plant with CO 2 

### 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 CO 2 

### 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 CO 2 

### - EOR projects 1



### ................................ ................................ .................... 5

- 2 Table 5

- 2 Es timated CO 2 

### effectiveness for selected CO 2 

### - EOR projects 5,6



### ................................ 5

- 9 Table 5

- 3 Average enhanced oil production per day per well for six largest CO 2 

### - EOR projects i



### ................................ ................................ ................................ .............................. 5

- 10 Table 5

- 4 Ratio of producers to injectors for selected CO 2 

### - EOR projects 5,6



### ............................ 5

- 11 Table 5

- 5 Capit al 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 functions 15



### ................................ ................................ ............. 5

- 22 Table 5

- 15 Comparison of EOR cost results 15,16



### ................................ ................................ ....... 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 des ign 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 CO 2 

### - ECBMR cost estimates from Wong, et al. 5



### ................................ ..................... 6

- 21 Table 6

- 12 Comparison of CO 2 

### - ECBMR cost results 5,14



### ................................ ........................... 6

- 21 Table 7

- 1 Capital and O&M cost estimation factors/fun ctions

................................ .................... 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 Incomple te 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 st orage 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 Deple

ted 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 lo w 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 cos t 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 P rocess 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 N 2 

### O sources

................................ ................................ ............................... 10

- 10 Table 10

- 3 Forest and baseline N 2 

### O And CH 4 

### emissions

................................ ....................... 10

- 11 Table 11

- 1 Productivity classes for Loblolly Pine in the southern United States

..................... 11

- 2 Table 11

- 2 Expected carbo n 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 Lob lolly 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 f or 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 o f 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 a bove

- 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 p roductivity case

................................ ................................ ................................ .............. 17

- 10 Table 18

- 1 Adoption of conservation

- tillage practices in the eastern corn belt and mid

south 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

- cy cle (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 intensive

till to no

- till for corn/soybean rotation in Indiana

................................ ................................ 19

- 4 Table 19

- 3 Reducti ons 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/soybea n rotation

................................ .................... 19

- 7 Table 19

- 5 Mean N rates for corn in the ten major U.S. corn producing states

-- 1995 13



### .......... 19

- 9 Table 19

- 6 Example spreadsheet output for estimating herbicide carbon equivalent emissions for corn/soybean rotation

................................ ................................ ................ 19

- 10 Table 19

- 7 Example spreadsh eet output for carbon equivalent emissions from changes in N 2 

### O emissions for corn/soybean rotation when changing from intensive

- till to no

till

................................ ................................ ................................ ................................ ...... 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

- 1 0 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 rotation s

................................ ................................ .. 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 N 2 

### O emissions due to both switching from intensive

- till to no

- till and increasing N ferti lizer 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 life

cycle (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 intensiv e - till to no

- till and increasing N fertilizer use

............................... 19

- 23 Table 19

- 17 Effects of increases in N rate and N 2 

### O 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 swi tching 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 fro m intensive

till 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 em issions 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 equivalen t emissions from changes in N 2 

### O 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

- 1 0 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 intensi ve - 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 N 2 

### O 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 increasin g 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 N 2 

### O 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 grea t plains

................................ ................................ ................................ ................................ .. 21

- 2 Table 21

- 2 Example process and economic input variables for switching from intensive

till 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 N 2 

### O emissions fo r 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 examp le spreadsheet output summarizing changes in GHG emissions due to switching from intensive

- till to no

- till with no change in N 2 

### O 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 N 2 

### O 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 N 2 

### O 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 intensive

till 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 N 2 

### O 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 su mmary

................................ ................................ ...... 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 N 2 

### O emissions

.............. 22

- 11 Table 22

- 10 Cotton costs and life

- cycle (LC) avoided GHG on a carbon equiva lent (CE) basis when changing from intensive

- till to no

- till with no change in N 2 

### O emissions

...... 22

- 13 Table 22

- 11 Cotton example spreadsheet sensitivity summary output when changing from intensive

- till to no

- till with no change in N 2 

### O emissions

................................ .......... 22

- 14 Table 2 3

- 1 CO 2 

### transport and storage cost comparison on a levelized annual CO 2 

### equivalent life cycle greenhouse gas avoided basis

................................ ......................... 23

- 1 Table 23

- 2 CO 2 

### capture, transport and storage cost comparison on a NPV equivalent life cycle greenhouse gas avoided basis

................................ ................................ ................ 23

- 3 T able 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 CO 2 

### 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 de tailed 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 res ervoir 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 Tab le 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 In centive 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

- ti ll 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 CO 2 

### mitigation mandates, energy companies need better cost information on CO 2 

### mitigation options, especially storage and sink enhancement options that involve non

- energy company operations. One of the major dif ficulties in evaluating CO 2 

### 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 CO 2 

### storage and sink e nhancement, including options for storing captured CO 2 

### , 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 fundin g 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 develo p 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 c aptured CO 2 

### 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 CO 2 

### storage processes and helped TVA develop economic models for comparing technologies and practices. The University of Tennessee Agricultural Policy Research Center, in collaboration w ith TVA, evaluated the economics of enhancing CO 2 

### 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 c oncentrated on CO 2 

### capture, with the assumption that CO 2 

### 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 gener ally 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 forIntroduction



### 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 devel op 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 o f 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 CO 2 

### 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 CO 2 

### storage options were sized to accommodate the CO 2 

### from our base

case capture plant (see the design basis in Chapter 3). In addition, the amount of forest and cropland required to sequester the same amount of CO 2 

### over a 100

- year planning horizon was calculated.



## 1.2 Conc epts 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 CO 2 

### 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 BedsIntroduction



### 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

CO 2 

### Injection Pipeline CO

2 

### (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 T able 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 t o 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

Wit h Power Systems

• Best potential CO 2 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 CO 2 • Ubiquitous a nd

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

— CO 2 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 CO

2 • ExcellentIntroduction



### 1

- 5

1.2.2 Oil Reservoir Storage with Enhanced Oil Recovery (EOR)
Shown below is the p reliminary block diagram for the EOR storage concept.

Distribution Oil Treaters (existing)

CO 2 

### Compression Recycle

Producing Well Operation Water Treatment CO 2 

### Injection Sales Oil Pipeline CO 2 

### (7,389 tpd )

CO 2 

### / Oil Separa tion

Figure 1
- 2 Preliminary Block Diagram for EOR Concept


### The rationale for including the EOR concept in the economic evaluation i s 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 CO 2 

### 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 Economi c 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 CO 2 done commercially today • Could often be

cheaper to obtain CO 2 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 CO 2 under pres sure • 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 CO 2

• Industry actively investigating the opti on of using captured CO

2 • Oil operations require continuous supply (versus intermittent)

• Issues with fluctuation in the quantity of CO 2 needed over timeIntroduction



### 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

CO 2 

### Injection Pipeline CO

2 

### (7,389 tpd )

Figure 1
- 3 Preliminary Block Diagram for Depleted Oil and Gas Reservoir Storage


### The rationale for including the Depleted Oil an d 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)

• Reservoir s have proven containment over geological time frames

• Knowledge about reservoir already exists • Today very few

reservoirs depleted

• Understanding risk of catastrophic or slow release of CO 2 • 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 plantsIntroduction



### 1

- 9

1.2.4 Coal Bed Storage
Shown below is a preliminary block diagram for the Coal Bed Stora ge concept.

Distribution

CO 2 

### Injection Pipeline CO

2 

### (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, CH 4 

### 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 Includin g Coal Bed Storage in the Economic Study

Merits Potential

Challenges Applicability Technical Maturity Data Availability Industrial Acceptance Compati bility

With Power Systems

• CH 4 by

- product makes option economically attractive

• CO 2 strongly sequestered by adsorption on coal matrix

• Worldwide large coal deposits
means potentially large CO 2 storage capacity • Enhanced gas

rec overy (EGR) methods for coal bed CH 4 exploitation require further refinement • Unclear as to

how many types of coal formations will be practical to use for coal bed CH 4 production • Injection of CO 2

into coal beds already used to enhance CH 4 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 CH 4 , where waste CO 2 produced by plant is injected into coalbed CH 4 reservoirs to produce more C H 4Introduction



### 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

CO 2 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 ratio nale 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 CO 2 

### in the ocean. Sufficient d ata 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 CO

2 , 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 issue s to be overcome

• Negatively perceived by non governmental 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 coastlineIntroduction



### 1

- 13

1.2.6 Forest Sink Enhancement
Shown in Figure 1

- 6 is a preliminary block diagram for the Forest Sink Enhancement concept.

CO 2
Forest Growth



### Forest

7,389 tpd CO

2 net Forest Fires,

pests, etc.

Carbon in trees and soil CO 2

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 bel ow. 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

econ omics 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 A vailability 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

• O pportunities 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 effect ive 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 large

scale projects are mainly deforestation avoidance • Still being

debat ed 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 plantIntroduction



### 1

- 15

1.2.7 Cropland Sin k 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 res idue 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 t he 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 CO 2 

### sequeste red 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 CO 2 

### , and (3) the reduced soil erosi on and the associated reduction of CO 2 

### emitted from eroded soil.



## Conservation- Tillage System & Residue Management

Crop Yield Crop

Residue Carbon Production Soil Organic

Matter (7,389 tpd CO 2 

## Net)

Eroded Soil CO 2 

## CO 2 

## CO 2

Figure 1
- 7 Preliminary Block Diagram for Cropland Sink Concept


### The rationale for including the Cropland Sink Enhanceme nt 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 so il 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 re ducing 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 Potenti al

Challenges Applicability Technical Maturity Data Availability Industrial Acceptance Compatibility

with power systems

• Relativ ely low projected cost/ton of CO 2

• Collateral benefits of conservation tillage

— improved soil quality, reduced soil erosion, improved water

use efficiency, improved crop productivity where well adapted • Possible need

for periodic use of conventional tillage t o maintain crop productivity, resulting in partial loss of sequestered CO 2

• Possible reversion to conventional tillage due to changes in land ownership

• Resistance to including biological sinks in GHG polices

• Poorly developed infrastructure for CO 2 credits a nd markets

• Good base for infrastructure • Excellent in well

drained 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% adoptio n achieved to date in U.S.

• Technology ready for rapid adoption, given additional economic incentives • Good for costs

of tillage systems

• Moderately good for CO 2 sequestration rates

• Lacking for equilibrium levels of sequestered carbon and time to equilibrium

• Good for CO

2 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 • Goo d

compatibility via combining farm

level CO 2 sequestration credits into bundles of sufficient size to match power project needsIntroduction



### 1

- 17

In conventional

- tillage systems, soil is plowed or otherwise thoroughly tilled , and all of the crop residues are mixed with so il. 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 t he atmosphere as CO 2 

### . 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 CO 2 

### 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 capture d CO 2 

### 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/EP RI. 2000. Evaluation of innovative fossil

- fuel power plants with CO 2 

### 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 CA LCULATING THE COST O F CAPTURING AND STORIN G CO 2



### General procedures for calculating the cost of capturing and storing CO 2 

### are overviewed in this chapter. They include:

• Calculating costs using revenue requirement methodology

• Defining th

e three cost bases used in this report: CO 2 

### captured, CO 2 

### avoided, and CO 2 

### equivalent life

- cycle (LC) greenhouse gas (GHG) avoided

• Combining CO 2 

### capture and storage costs

• Accounting for differences in timing of CO 2 

### sequestration profiles and for leaking

r eservoirs
Specific procedures are provided in Chapter 3.


## 2.1 Revenue Requirement Methodology
Costs of storing captured CO 2 

### were calculated using the same general approach as for CO 2 

### capture costs that have already been calculated by EPRI and DOE for severa l power plant CO 2 

### capture technologies 1 

### . This allowed CO 2 

### capture and storage costs to be combined on an equal basis.

2.1.1 Approach for CO 2 

### Capture
In the DOE/EPRI project, EPRI revenue requirement (RR) methodology was used 2 

### . Revenue requirement methodo logy 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, uti lities 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 CO 2



### 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 capita l, 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 CO 2 

### capture (Case 3b of reference 1) and for the same IGCC plant converted to capture CO 2 

### (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 CO 2 

### . 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 captu re and non

capture IGCC plants was used as a basis for calculating the cost of CO 2 

### 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 7.09Methodologies for Calculating the Cost of Capturing and Storing CO 2



### 2

- 3 2.1.2 Adjustment for CO 2 

### Storage
In calculating the costs of storing captured CO 2 

### , the RR methodology for CO 2 

### capture was generalized to accommodate options for enhanced revenues from CO 2 

### storage. These adjustments were required because CO 2 

### storage options such as enhanced oil recovery (EOR) and enhanced coal bed methane recovery (ECBM R) generate revenues that at least partially offset the cost of CO 2 

### storage. This required that the RR methodology for CO 2 

### capture be generalized as follows:

Levelized RR = LCC + O&M costs

- Enhanced revenues

= Levelized annual net cost of storing CO 2 

### (2)
A positive net storage cost equals the breakeven CO 2 

### tipping fee for EOR. A negative net storage cost equals the breakeven CO 2 

### purchase price for EOR.

2.1.3 Adjustment of Levelized Carrying Charge Factor as a Function of Discount Rate
Costs of captu

ring and storing CO 2 

### 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 CO 2 

### storage and sink enhancement options differing in timing of costs and in timing and permanence of CO 2 

### storage and sink enhancement (see Section 2.4). The discount rate used in Section 2.4 calculations needs to be consistent with the LCC fa ctor. The discount rate and LCC factor are related by Equation (3).

LCC factor = 0.0738 + 0.01 r + 0.0004 r 2 

### (3)
Equation (3) is based on the regression in Figure 2

- 1. Levelized carrying charge factors were calculated for the range of after

- tax dis count 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 CO 2



### 2

- 4 y = 0.0004x 2 

### + 0.01x + 0.0738 R 2 

### = 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 CO 2 

### Storage Costs on Cost of Electricity

The COE for the IGCC CO 2 

### capture plant was adjusted for the net cost of CO 2 

### storage (i.e., storage co sts minus enhanced oil or gas revenues). The increase in COE due to CO 2 

### storage was calculated by dividing the total revenue requirement for CO 2 

### storage by the total MWh of electricity produced. Consistent with revenue requirement methodology, CO 2 

### storag e 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 CO 2 

### storage, and thereby, reduce any in

crease in electricity prices that a regulated utility would be allowed to charge to cover CO 2 

### 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 ga s revenues are large enough to result in a negative revenue requirement for storing CO 2 

### . 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 norm ally be allowed to charge to cover CO 2 

### storage costs, andMethodologies for Calculating the Cost of Capturing and Storing CO 2



### 2

- 5 • Reduce part of the increase in electricity prices that a regulated utility would normally be allowed to charge to cover CO 2 

### capture costs.


## 2.2 Capture Costs on CO 2 

## Captured, CO 2 

## Avoided, and Life
- Cy cle GHG Avoided Bases

2.2.1 CO

2 

### Captured Basis
The cost per tonne of CO 2 

### captured was calculated by subtracting the cost of electricity (COE, $/MWh) for the IGCC reference plant without CO 2 

### capture from the COE for the IGCC plant with CO 2 

### capture and divid ing by the amount of CO 2 

### captured (tonne/MWh). Assuming no losses of captured CO 2 

### during transport and storage, this calculation also provides the capture cost per tonne of CO 2 

### stored. Inputs for calculating CO 2 

### capture costs on a CO 2 

### captured basis

are presented in Table 2

- 2. For this case, the CO 2 

### capture cost on a CO 2 

### captured basis = (55.08

- 43.98)/0.763 = $14.55/tonne CO 2 

### captured.
Table 2
- 2 Calculation of CO 2 capture costs on CO 2 captured and CO 2 avoide d bases *
Based on IGCC example from DOE/EPRI report 1

Capture plant Reference plant

COE, $/MWh 55.08 43.98

CO 2 produced, tonne/MWh 0.836 0.718 CO 2 captured, tonne/MWh 0.763 0.000

Direct CO

2 emitted, tonne/MWh 0.073 0.718 LC GH G emitted, tonne CO 2 eq./MWh (see Table 3) 0.111 0.750

Cost, CO 2 captured basis: Costs CO 2 capture cost (CO 2 captured basis), $/tonne CO 2 captured 14.55

Costs, CO 2 avoided basis: CO 2 capture cost (CO 2 avoided basis), $/tonne CO 2 avoided 17.21 CO 2 capture cost (LC GHG avoided basis), $/tonne CO 2 eq. avoided 17.37

* Assumes 80% capacity factor.Methodologies for Calculating the Cost of Capturing and Storing CO 2



### 2

- 6 2.2.2 CO

2 

### Avoided Basis The primary difference in capturing CO 2 

### for commercial markets versus capturing CO 2 

### 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 CO 2 

### emissions, which is precisely what we want to avoid. We can account for this “energy penalty” by calculating cost s on a CO 2 

### avoided basis. As shown in Figure 2

- 2, due to the extra energy required to capture CO 2 

### , the amount of CO 2 

### emissions avoided is always less than the amount of CO 2 

### captured. Therefore, capturing CO 2 

### for purposes of sequestration requires more em phasis on reducing energy inputs than in traditional commercial processes.



## CO 2 

## Avoided during CO 2 

## Capture

0 0.2 0.4 0.6 0.8 1 1.2 Capture Plant Reference Plant

CO 2 

### Produced, tonne/MWh CO2 Emitted
CO2 Captured

CO 2 

### Captured CO 2 

### Avoided

Figure 2
- 2 Relationships among CO 2 produced, captured, and avoided when comparing power plants with and without CO 2 capture (based on Table 2
- 2)


### The cost per tonne of CO 2 

### avoided in the CO 2 

### capture process was calculated with Equation (4) from the EPRI/DOE report 1 

### : 

# ( ) ( )

( ) ( ) ( ) 4 / / / $ / $

2 2 2 

### MWh emitted CO tonne MWh emitted CO tonne MWh

MWh Avoided CO of Cost

capture with capture without capture without capture with



### − −
=



### Inputs for calculating CO 2 

### capture costs on a CO 2 

### avoided basis are presented in Table 2

- 2. For this case, the cost of CO 2 

### avoided = (55.08

–43.98)/(0.718

- 0.073) = $17.21/tonne CO 2 

### avoided.Methodologies for Calculating the Cost of Capturing and Storing CO 2



### 2

- 7 2.2.3 LC GHG Avoided Basis

The calculation of CO 2 

### 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 CO 2 

### equivalent LC GHG emitted/MWh instead of tonne CO 2 

### directly emitted from the power plant/MWh. Life

- cycle GHG emissions are greater than direct CO 2 

### emissions for two reasons. First, green

house gases in addition to CO 2 

### 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 fr om power plant operations; this boundary expansion was required because of the energy penalty and associated added coal required for CO 2 

### capture and because of the energy use associated with added equipment required for CO 2 

### capture. Inputs for calculating CO 2 

### 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 CO 2 

### equivalent LC GHG avoided. Calculation of LC GHG emissions (values in the d enominator 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) report 7



### . 

## 2.3 Combining Capture and Storage Costs
The previous secti

on 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 G HG avoided basis.

2.3.1 CO

2 

### Captured Basis

Costs associated with CO 2 

### compression, transportation, and storage are included in the cost of electricity (COE, $/MWh) from a CO 2 

### capture plant. The cost per tonne of CO 2 

### captured is calculated by subtracting th e COE for the reference power plant without CO 2 

### capture from the COE for the power plant with CO 2 

### capture, including costs for compression, transportation, and storage, and dividing the difference by the amount of CO 2 

### captured (tonne/MWh).Methodologies for Calculating the Cost of Capturing and Storing CO 2



### 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 CO 2

Capture (Case 3a) Without CO 2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N 2 0 CH 4 CO 2 N 2 0 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 CO 2 Equivalent 243,338 3,433 66,762 Total Power Plant CO 2 Equivalent 2,167,749 3,530 60,262 Total (mg CO 2

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 CO 2 to Pipeline Spec CO 2 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 CO 2 Equivalent 1,605 23 438 CO 2 Compression Total ( CO 2 Equivalent ) 2,066
Transportation & Sequestration of Captured CO 2 CO 2 Transportation

Pipeline Construction 913 0.0 0.0 Pipeline Decommissioning 91 0.0 0.0 Total 1,005 0 0 CO 2 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 CO 2 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 CO

2 equivalent emitted/kWh 0.111 0.750 0.639 Compression CO 2 equivalent emitted, tonne/yr 2,066 CO 2 equivalent avoided, tonne/yr 1,807,446 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 133,538 Total CO

2 equivalent emitted, tonne/yr 135,604 Total System CO 2 equivalent avoided by capture, tonne/yr 1,807,446 CO 2 equivalent emitted from storage, tonne/yr 135,604 CO 2 equivalent LC avoided, tonne/yr 1,671,842 CO 2 equivalent LC avoided, % of CO 2 captured 77.5% CO 2 equivalent LC avoided, % of CO 2 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 & StorageMethodologies for Calculating the Cost of Capturing and Storing CO 2



### 2

- 9 2.3.2 CO

2 

### Avoided Basis (CO 2 

### Capture and Storage Combined)

In addition to the inclusion of CO 2 

### compression, transportation, and storage costs as descri bed in section 2.3.1 above, this basis also considers the CO 2 

### emitted during CO 2 

### storage. Referencing Equation 4, including storage costs on a CO 2 

### avoided cost basis will increase the $/MWh with capture 

### and the tonne CO 2 

### emitted/MWh with capture 

### terms.

2.3. 3 GHG Avoided in CO 2 

### 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 captur e 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 CO 2 

### emissions from the refe rence and capture power plants. The LC GHG emissions avoided via CO 2 

### 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 comb ined (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 CO 2 

### was divided by the annual tonnes of CO 2 

### equivalent GHG avoided via capture and storage processes combined. For the EOR example in Table 2

- 3, the annual combined net cost of CO 2 

### capture and storage was $6.70 million (data not shown). The annual tonnes of CO 2 

### equivalent GHG avoided in capture and storage processes combine d was 1.67 million (see next paragraph for details). Dividing annual costs by annual tonnes gave a net cost of $4.01/tonne CO 2 

### 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 CO 2 

### 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 CO 2 

### equivalent GHG avoided via the capture process (1,807,446 tonnes CO 2 

### equi valent LC GHG avoided/yr). The LC GHG emissions from EOR CO 2 

### storage were 135,606 tonnes CO 2 

### equivalent LC GHG/yr, resulting in 1,807,466

- 135,606 = 1,671,840 tonnes CO 2 

### equivalent LC GHG avoided/yr for the CO 2 

### capture and storage processes combined.

### Methodologies for Calculating the Cost of Capturing and Storing CO 2



### 2-10 

## Life-Cycle (LC) GHG Avoided: CO 2 

## 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 CO 2 

### equivalents/MWh CO2 Emitted--Power Plants

∆ GHG w/ LC Analysis-- Power Plants LC GHG Emitted--CO2 Transport & Storage CO2 Captured

CO 2 Captured LC GHG Avoided



### Figure 2-3 Relationships among GHG produced, CO 2 

### captured, and GHG avoided on a LC basis for CO 2 

### capture and storage (via EOR) combined; based on Tables 2-2 and 2-3


### In the case of ocean storage, the portion of injected CO 2 

### predicted to leak back out of the ocean (3.1 percent in 100 years from an MIT model) wa s subtracted from the GHG emissions avoided during the CO 2 

### 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 CO 2 

### capture and storage on a LC GHG avoided basis.

2.3.4 Costs of CO 2 

### Storage on a Life-Cycle GHG Avoided Basis (Storage Only)
The cost per tonne of CO 2 

### stored was calculated on a life- cycle GHG avoided basis (storage system only) by dividing the total annual revenue requirement for CO 2 

### storage by the difference between annual tonnes CO 2 

### stored and LC GHG emitted due to the storage operation (i.e., CO 2 

### compression, transportation, and sequestration). Examples of LC GH G emissions from CO 2 

### storage operations are presented for base cases in Table 2-4. This analysis included LC GHG emitted from energy consumption during equipmen t manufacture, transportation, construction, and decommissioning, and during the storage oper ation. Detailed LC GHG emissions from EOR storage (base case) are presente d in Figure 2-3. Detailed LC GHG emissions from all the basecase CO 2 

### storage options in Table 2-4 are presented in Appendix A.Methodologies for Calculating the Cost of Capturing and Storing CO 2



### 2

- 11 Table 2
- 4 Greenhouse gas emissions from CO 2 storage operations

CO 2 Storage Option Direct CO 2 emitted Total LC GHG emitted

(includes direct CO 2 emissions)
million tonnes CO 2 eq./y Storage of CO 2 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 CO 2 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 CO

2 /y

** Assumes 100 km from power plant to ocean shor e and 100 km from shore to ocean injection point; and storage of 6.474 million tonnes CO 2 /y.



## 2.4 Comparing the Economics of CO 2 

## Storage and Sink Enhancement Options Differing in Timing and Permanence of Sequestration
Carbon dioxide storage and sink enhan cement options differ greatly in timing of costs, the uptake or sequestration of CO 2 

### and, in some cases, leakage of CO 2 

### back into the atmosphere. For options involving storage of captured CO 2 

### , the primary costs and amounts of CO 2 

### stored are levelized over

the book life of the CO 2 

### capture and storage plants which is assumed to be 20 years in this project. With storage of captured CO 2 

### , there are also relatively small monitoring costs after 20 years, and in some cases, there may be additional transaction cos ts after 20 years. In some cases such as ocean storage, there are leaks of CO 2 

### back to the atmosphere that occur after 20 years. The costs and CO 2 

### 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 CO 2 

### 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 o ver 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 co sts, and perhapsMethodologies for Calculating the Cost of Capturing and Storing CO 2



### 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 CO 2 

### sink reduces in size, res ulting in CO 2 

### 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 CO 2 

### and CO 2 

### sink enhancement in forests and cropla nd. There have been many suggestions on how to handle this challenge 4, 6



### . R

emovals and emissions/leaks are treated as separate events. The idea is that when one removes a ton of CO 2 

### , one receives the going price of CO 2 

### . When a ton of CO 2 

### is released, t he owner of this CO 2 

### must then purchase a credit from elsewhere at the going price. CO 2 

### 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 l ong as CO 2 

### can be emitted in the atmosphere for free, there will be no incentive to sequester CO 2 

### on a large

- scale.
In order to implement this approach, one must make explicit assumptions concerning CO 2 

### prices and discount rates. We argue that no matter w hat methodology one adopts, they all must make assumptions about how to deal with CO 2 

### prices and discount rates. Some methods seem to avoid this task by making these assumptions implicitly. We prefer to confront the issue of the long

term value of CO 2 

### st orage 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 CO 2 

### storage and sink

en hancement options differing in timing of costs and in timing and permanence of storage and sink enhancement is shown below. This approach provides a breakeven CO 2 

### price ($/tonne of CO 2 

### sequestered) for each CO 2 

### storage and sink enhancement option that ref lects differences in timing of costs and in timing and permanence of CO 2 

### storage and sink enhancement. If the calculated breakeven CO 2 

### price is less than the market CO 2 

### price, then the proposed project has favorable economics. However, if the calculated breakeven CO 2 

### price is greater than the market CO 2 

### 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 CO 2 

### storage and sink enhancement options di ffering in timing and permanence is calculated as follows 3 

### :



# ( ) ( ) ( ) 

# ∑ =

= −



### + = 100

0 

### 1 t

t t



### r t a t p NPV 

### (5)

where p is the CO 2 

### 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 o f cases with constant and variable CO 2 

### prices, we concluded that considering variable CO 2 

### prices is beyond the scope of this project. Assuming a constant CO 2 

### price (p o 

### ) results in the following form of Equation (5):Methodologies for Calculating the Cost of Capturing and Storing CO 2



### 2

- 13 

# ( ) ( ) t t

t o 

### r t a p NPV −

=

= 

### + = 

# ∑ 1

100

0 

### (6)

Th e relative value of CO 2 

### offsets differing in timing and permanence is compared by setting the NPV of CO 2 

### abatement as calculated in Equation (6) equal to the discounted cost ( c ) of achieving the CO 2 

### offset and solving for a constant breakeven price as foll ows 5 

### : 

# ( ) ( ) ( ) ( ) 

# ∑ ∑ =

= − − =

= 

### +

= + 100

0 100

0 

### 1 1 t

t t

t t

t o 

### r

t c r t a p 

### (7)

and 

# ( ) ( )

( ) ( ) ion sequestrat CO of Cost

r t a r t c p Breakeven

t t t t

o 2 100

0 100

0 

### 1 1

= + +

= 

# ∑ ∑

= − =

− 

### (8)

Equation (8) is used in this project to compare breakeven prices (i.e., costs) for CO 2 

### storage and sink enhancement options differing in timing of costs and in timing and permanence of CO 2 

### 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 CO 2 

### 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 CO 2 

### storage and sink enhancement options included in this project.
Equation (8) reduces to a ratio of total CO 2 

### abatement costs to total avoided emissions for cases in which annual CO 2 

### storage costs and annual avoided emissions are in the same proportion over time. For the CO 2 

### capture and storage options in this project, CO 2 

### abatement costs and CO 2 

### avo ided emissions are levelized over time for years 1

- 20. This means that for years 1

- 20 of the CO 2 

### capture and storage options, the breakeven price is the same on a discounted and a non

discounted basis. However, when monitoring and/or transaction costs ar e 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 CO 2 

### (Table 2

- 1) was also used in base cases for cropland and forestry CO 2 

### sink enhancement options. The rationale for using the same discount rate for the CO 2 

### capture/storage and CO 2 

### 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 CO 2 

### capture and storage or CO 2 

### sink enhancement.Methodologies for Calculating the Cost of Capturing and Storing CO 2



### 2

- 14 

## 2.5 References

1 DOE/EPRI. 2000. Evaluation of innovative fossil fuel power plants with CO 2 

### removal. EPRI, Palo Alto, California; U.S. Department of Energy

– Office of Fossil Energy, Germantown, Maryland, and U.S. Departm ent 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 & Poli cy

. 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 Conf erence 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. Murd iyarso, 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 fore stry. 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 Cha nge 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. Johnso n

(eds.). Cambridge University Press, Cambridge, United Kingdom and New York, N ew Y ork , USA, 881 pp

. , 2001.

### 3

- 1 

# 3 STORING CAPTURED CO 2 

# — BASIS & APPROACH

3.1 Technologies Evaluated
The following transportation and injection processes for captured CO 2 

### 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 oce an 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 s ame was thought to be true for coalbed methane. However, a recent IEA Greenhouse Gas R&D Programme Report 1 

### assessed the potential of enhanced coalbed methane recovery with CO 2 

### 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, indicat es 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 CO 2 

### 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 s urveys, “Joint Association Survey on Drilling Costs” 2 

### and “Costs and Indices for Domestic Field Equipment and Production Operations” 3



### ,Storing Captured Co 2 — 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 CO 2 

### . 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 anal

yses and life cycle analyses, again in a spreadsheet format.



## 3.3 Common Design Basis

A nominal 500 MW e 

### gross integrated gasification combined cycle (IGCC) plant operating at an 80 percent capacity factor was utilized as the production source of CO 2 

### . This was based on the DOE/EPRI’s recent study on the “Evaluation of Innovative Fossil Fuel Power Plants with CO 2 

### 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 CO 2 

### Removal

. ”Storing Captured Co 2 — Basis & Approach



### 3

- 3 Tab le 3
- 1 Summary of parameters for IGCC power plant with CO 2 removal

Parameter Unit Value Thermal Input, HHV 10 6 Btu/h 3,723 Gross Power Output MW 490.4 Net Power Output MW 403.5 Efficiency, HHV % 37.0 Capa city Factor % 80
CO 2 Captured t/d 7,389
million scm/d 3.76
CO 2 Emitted kg/kWh 0.073
CO 2 Avoided in Capture t/d 6,246
CO 2 Capture Cost $/t captured 14.55
CO 2 Capture Cost $/t avoided in capture 17.21

Plant Life y 20 Capital Charge Factor % 15.0
Fu el 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 (L COE) $/MWh 55.08

10 6 Btu = 1.06 GJ


### The baseline IGCC plant produces two streams of CO 2 

### 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) CO 2 

### streams are furtherStoring Captured Co 2 — Basis & Approach



### 3

- 4 compressed and dehydrated in a multi

- stage, intercooled compressor to 83 bar (1,200 psia). The amount of CO 2 

### recovered from the IGCC plant that must be sequestered is 7,389 tonnes per day

. The existing pipeline specification (152 bar,

- 40 ° 

### C dewpoint, N 2 

### <300 ppmv, O 2 

### <40 ppmv, Ar<10 ppmv) was used for the quality of the CO 2 

### . The DOE/EPRI study is consistent with this except for the CO 2 

### pressure. The DOE/EPRI study was based on compressing the CO 2 

### 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 CO 2 

### at 83 bar (1200 psia) and 41 ° C (105 ° F) is above and to the liquid si de 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 CO 2 

### mass is ensured of retainin g flow properties approximating a liquid. This boost compression adds an additional power requirement of 2,650 kW e 

### . The CO 2 

### stream is dried to a

- 40 ° C dewpoint and contains N 2 

### <300 ppmv, O 2 

### <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, transporta tion, 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 CO 2 

### Removal

. ” 5Storing Captured Co 2 — Basis & Approach



### 3

- 5 Case 3a was based on an IGCC power system with an output of 404 MW e 

### 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 CO 2 

### (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 dis cussed 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 c oal 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 CH 4 

### released is expected to be about 10 Nm 3 

### /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

CH 4 50

VOCs 2

O 2 7

N 2 37

CO 2 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 r eleased 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 a

ssumed that 60 percent of the methane released can be collected as combustible gas and burned usefully to generate power or is flared 7



### . On the basis of the analysis of mine methane and 60 percent collection, the atmospheric emissions of unburned gas associ ated with the production of one tonne of clean coal is 2.86 kg of CH 4 

### and 0.63 kg of CO 2 

### .Storing Captured Co 2 — 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 CH 4 

### and 0.5 0 kg of VOCs per tonne of clean coal. This combustion will yield 33.2 kg of CO 2 

### per tonne of coal. Emissions of CH 4 

### and N 2 

### O from burning mine gas are calculated based on the EPA emission factors for gas turbines resulting in emissions of CH

4 

### of 3.4x10 - 3 

### lb/tonne of coal and N 2 

### O 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 tonn es 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 CO 2 

### . Other emiss ions 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 CO 2 

### abatement by shift conversion and scrubbing of CO 2 

### from the fuel gas prior to the combustion of hydrogen in a gas turbine. The CO 2 

### emissions are derived from the mass balance data reported in Reference 1. N 2 

### O content of the IGCC gas turbine exhaust is estimated to be 0.5 ppm. 6 

### The N 2 

### O emissions were calculated from the design flow rate of stac

k gas of 510 Nm

3 

### /s, which is equivalent to 12.9x10 9 

### Nm 3 

### /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 construct ion 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 available 6 

### showed typical steel, cement, and aggregate consumption in the ratio 1:1:6; therefore, th e 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 information 6 

### indicates a steel utilization in power plant projects of the order of one tonne of steel per $5 0,000 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 w ith the energy requirement of the construction materials.Storing Captured Co 2 — 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 CO 2 

### is emit ted from the manufacture of one tonne of cement. Therefore, an additional 6,000 tonnes of CO 2 

### 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 i s 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 th

at 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 CO 2


### 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 CO 2 

### compression uni t 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 CO 2 

### The amount of steel for the pipeline construction was based on the amount of steel needed for the size pipe use d, 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 th e CO 2 

### 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 CO 2 

### 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 CO 2 

### emitted by the tankers and due to boil off. The methodology used to calculate the CO 2 

### emitted from these sources is described in Section 9.4.3.Storing Captured Co 2 — Basis & Approach



### 3

- 8 Table 3
- 3 Greenhouse gas life
- cycle analysis



### With CO 2 

### 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 CO 2 Equivalent 243,338 3,433 66,762 Total (mg CO 2 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 CO 2 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 CO 2 Equivalent 1,605 23 441 CO 2 Compression Total ( CO 2 Equivalent ) 2,068
Transportation & Sequestration of Captured CO 2 CO 2 Transportation

Pipeline Construction 913 0.0 0.0 Pipeline Decommissioning 91 0.0 0.0 Total 1,005 0 0 CO 2 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 CO 2 Equivalent 129,759 210 3,569 Transportation & Sequestration Total ( CO 2 Equivalent ) 133,538 GHG Emissions tonnes/yearStoring Captured Co 2 — Basis & Approach



### 3

- 9 

## 3.5 References

1 “Enhanced Coal Bed Methane Recovery with CO 2 

### Sequestration

. ” IEA Greenhouse Gas R&D Programme

. Report Number PH3/3, August 1998.

2 “1998 Joint Association Survey on Drilling Costs

. ” American Petroleum I nstitute, Policy Analysis and Statistics Department, November 1999

. http://www.api.org/axs

api/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 Inn

ovative Fossil Fuel Power Plants with CO 2 

### 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 Fu

el 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://w ww.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 CO 2 

### 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 o ptions.



## 4.2 State of the Art
Over 110 million standard cubic meters (scm) per day of CO 2 

### are transported by pipeline in the United States, frequently for distances greater than 100 km. Details of currently operating CO 2 

### pipelines in the United States are given in Table 4

- 1

. 1 , 2 , 3 , 4 , 5 , 6Pipeline Transport



### 4

- 2 Table 4
- 1 CO 2 pipelines in the United States

Name Operator Route CO 2 Source Length (km) Diameter (in) Capacity (10 6 scm per day) Cortez Pipeline 2 Kinder Morgan CO 2 McElmo Do me

to Denver City CO 2 Hub Natural CO 2 deposit 311 30 28

McElmo Creek Pipeline 2 ExxonMobil McElmo Dome to McElmo Creek Unit (UT) Natural CO 2 deposit 25 8 2

Bravo Pipeline 2 BP Bravo Dome to Denver City CO 2 Hub Natural CO 2 deposit 135 20 11

Sheep Mountain I 2 BP Sheep Mountain Field to Rosebud connection with Bravo Dome Natural CO 2 deposit 114 20 9

Sheep Mountain II 2 BP Rosebud connection to Denver City CO 2 Hub and onward to Seminole San Andres Unit (TX) Natural CO 2 deposit 139 24 14

Central Basin Pipelin e 2 Kinder Morgan CO 2 Denver City CO 2

Hub to McCamey, TX - - 26,16 17

Este Pipeline 2 ExxonMobil Denver City CO 2 Hub to Salt Creek, TX - 74 12,14 7

Slaughter Pipeline 2 ExxonMobil Denver City CO 2 Hub to Hockley County, TX - 25 12 5

West Texas Pipeline 2 Tri nity Pipeline Denver City CO 2 Hub to Reeves County, TX - 79 12,8 3Pipeline Transport



### 4

- 3

Table 4
- 1 (continued) CO 2 pipelines in the United States

Name Operator Route CO 2 Source Length (km) Diameter (in) Capacity (10 6 scm per day) Llano Lateral Pipeline 2 Trinity Pipeline run s off Cortez main line to Llano, NM Natural CO 2 deposit 33 12,8 3

Canyon Reef Carriers Pipeline 2 Kinder Morgan CO 2 McCamey, TX to

SACROC field - 87 16 7

Val Verde Pipeline 5 , 6 PSCC connects Mitchell, Gray Ranch, Pucket and Terrell gas processing faciliti es to Canyon Reef Carriers main line Gas processing

facilities 51 10 4

Weyburn Pipeline 3 Dakota Gasification Company Great Plains

Synfuels plant (Beulah, ND) to Weyburn field (Saskatchewan, Canada) Coal gasification

plant 127 14,12 3

Choctaw Pipeline 4 De nbury Resources Jackson Dome

to Bayou Choctaw Field, LA Natural CO 2 deposit 115 20 6

1 in = 0.0254mPipeline Transport



### 4

- 4 Transported CO 2 

### is most commonly used for enhanced oil recovery (EOR). The use of CO 2 

### for EOR is a proven technology with 72 CO 2 

### floods in the United St ates. 7 

### Most of these floods are dependent upon naturally occurring CO 2 

### , which is obtained from high

- pressure, high

- purity underground deposits. The most important of these natural CO 2 

### deposits, in decreasing order of current production, are the McElmo Do me, the Bravo Dome, the Sheep Mountain Field and the Jackson Dome. 4 , 8 

### A small fraction of the CO 2 

### supply comes from anthropogenic sources, including the Mitchell, Gray Ranch, Pucket and Terrell gas processing facilities in the southern Permian basin and t he Great Plains coal gasification plant at Beulah, North Dakota. 3 , 6


### The operation of the Canyon Reef Carriers pipeline, one of the first CO 2 

### pipelines constructed for EOR, provides a reference for future CO 2 

### 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 du e 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 occ urred 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 impor tant technical consideration in the design of pipelines for transport of supercritical CO 2 

### is that the CO 2 

### remains above critical pressure. This can be achieved by means of recompression of the CO 2 

### at certain points along the length of the pipeline. Rec ompression 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 CO 2 

### pipeline runs for 330 km (205 miles) fr om 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 t he 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 CO 2 

### for pipelin e transport is taken from Case 3a of the DOE/EPRI Report on CO 2 

### removal from fossil fuel power plants. 12 

### This case is used for the design basis since potential CO 2 

### sources from a coal

- based power plant would most probably be associated with an IGCC plant. CO 2 

### recovery from IGCC is most economical because of the CO 2 

### 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 CO 2 

### 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 CO 2 

### 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 CO 2 

### storage process involves the CO 2 

### being tran sported 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 CO 2 

### 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 CO 2 

### 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: CO 2 

## mass flow rate Pipeline length CO 2 

## in let pressure CO 2 

## 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: CO 2 

## density CO 2 

## 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 CO 2 

### pressure is set equal to 152 bar, which is equivalent to the pressure of the CO 2 

### supplied by the base case IGCC plant. Based on a re commendation that the pipeline CO 2 

### pressure not be allowed to fall below 103 bar, 13 

### this latter value is used for the pipeline outlet CO 2 

### pressure. The maximum allowable pressure drop per unit length ( Δ P/ Δ L) is found as the difference between the pipeline inlet and outlet CO 2 

### pressures divided by the pipeline length.
Next, based on an assumed ambient temperature of 25oC, the CO 2 

### density and viscosity are calculated. The CO 2 

### density ( ρ ) is calculated to be 884 kg/m 3 

### , using a correlation based on data from t he National Institute of Standards and Technology (NIST) for a temperature range of 5 toPipeline Transport



### 4

- 6 27oC and a pressure range of 80 to 140 bar. 14 

### The CO 2 

### viscosity (μ) is found, from a correlation published by Nihous and Bohn, 15 

### to be 6.06 x 10 - 5 

### N

- s/m 2



### . The pipeline diameter is calculated using the equations for pressure drop and head loss due to frictional resistance in a pipe, assuming turbulent flow. T his 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/ π μD

where m is the CO 2 

### 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 for mula

D 5 

### = 32fm

2 

### / π 2 

### ρ ( Δ P/ Δ L)

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 CO 2 

### mass flow rate.



### Diameter as a Function of CO 2 

### Mass Flow Rate

0 5 10 15 20 25 30 35 40

0 20 40 60

Mass flow rate (Mt CO 2 

### /y)Pipe diameter (in)

1 in = 0.0254m Figure 4
- 2 Diameter for the base case as a function of CO 2 mass flow ratePipeline Transport



### 4

- 7

4.4.2 Cost Calculations

The amount of cost data on CO 2 

### pip elines 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 CO 2 

### pipelines. This is ad equate given that there is little difference between land construction costs for these two types of pipeline. 8 

### It is worth noting, though, that CO 2 

### pipelines might be slightly more expensive because of the greater wall thickness needed to contain the CO 2 

### , 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 t he 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 f or 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, andPipeline Transport



### 4

- 8 miscellaneous costs appear to be random percentages o f 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 expert 13 

### the const ruction cost for CO 2 

### pipelines should be close to $12,400/in/km ($20,000/in/mile). One possible reason for this lower CO 2 

### pipeline construction cost estimate is that CO 2 

### pipelines are currently constructed in sparsely populated areas. Another is that the rock in New Mexico and West Texas where most CO 2 

### 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 R 2 

### = 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 abou t $40,000 to $60,000 per month to operate 480 km (300 miles) of pipeline and that this figure shou ld be doubled to account for associated overhead costs. 13 

### Taking the higher value to be on the conser vative side, O&M costs are estimated to be $3,100/km ($5,000/mile) per year, independent of pipelin e diameter. It should be noted that this O&M cost estimate does not account for pumping or its a ssociated costs.
Total pipeline construction cost is found usi ng 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 CO 2 

### is found by annualizing the construction cost using a capital charge rate of 15 percent per year and addi ng this to the annual O&M cost. Figure 4-6 shows the co st per tonne of CO 2 

### , calculated for the base case, as a function of CO 2 

### 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 CO 2 

### /yr)Cost ($/t CO2)

Figure 4
- 6 Total cost per tonne of CO 2 for the base case as a function of CO 2 mass flow rate



## 4.5 Design Basis

4.5.1 Pipeline Design The methodology described in Sec tion 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 b ases for pipeline transport

Parameter Unit Pipeline Transport Base Case Pipeline Transport Sensitivity Case Pipeline Length km 100 300 CO 2 Inlet Pressure MPa 15.2 15.2 CO 2 Outlet Pressure MPa 10.3 10.3 Pressure Drop per Unit Length* Pa/m 49 16

Pipe Di ameter* inches 11.2 13.8 Nominal Pipe Size* inches 12 16 * calculated 1 in = 0.0254mPipeline 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 Secti on 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 Sensi tivity (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 co st 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 CO 2 

### for the base and sensitivity cases are $1.78 and $6.49. These are converted to CO 2 

### 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 CO 2 

### were taken from the studies listed in Table 4

- 4.Pipeline Transport



### 4

- 12 Table 4
- 4 Overland pipelines’ characteristics

Study CO 2 flow rate (Mt/yr) Initial CO 2

pressure (bar) Diameter

(m) Length

(km) Recompression

station included IEA aquifer 18 3.90 208 0.400 30 No IEA depleted reservoir 18 3.16 110 0.400 50 No

British Coal 19 3.63 136 0.350 425 Yes Weyburn 10 2.00 170 0.305 127 No



### A pumping station

is required for the ‘British Coal’ CO 2 

### 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 ig nored. 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 CO 2 

### mass flow rate. Also shown in the figure , is the value of pipe diameter, for a specific CO 2 

### 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 CO 2 

### Mass Flow Rate

0 5 10 15 20 25 30 35 40

0 10 20 30 40 50 60 Mass flow rate (Mt CO 2 

### /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 studie s. Our model shows generally lower costs, especially when compared to the ‘British Coal’ study. The discre pancy can be attributed to the additional costs associated with pipelines located in the more populated areas of Eur ope 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 CO 2 

### /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 CO 2 

### EOR in U.S.,” Oil & Gas Journal , May 14, 2001.
2 Kinder Morgan CO 2 

### Company, “CO 2 

### Transportation,” [Online document], 2001, [cited Apr. 2002], Available HTTP: http://www.kne.com/co2/transport.cfm
3 International Energy Agency Green house Gas R&D Programme (IEAGHG), “Weyburn CO 2 

### -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. Ri gg, “Natural analogs for geologic storage of CO 2 

### : An integrated global research program,” pr esented at the First National Conference on Carbon Sequestration, Wash ington D.C., May 15-17, 2001.

5 Petro Source Cor poration, “Val Verde CO 2 

### 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 CO 2 

### Company, “CO 2 

### Supply,” [Online document], 2001, [cited Jan. 2002], Available HTTP: http://www.kne.com/co2/supply.cfm
9 Gill, T.E., “Canyon Reef Carriers Inc. CO 2 

### pipeline: Description of 12 years of operation,” presented at the ASME Pipelin e Engineering Symposium, 1985.
10 Hattenbach, R.P., Wilson, M. and K. Brow n, “Capture of carbon dioxide from coal combustion and its utilization fo r enhanced oil recovery,” in Greenhouse Gas Control Technologies , Elsevier Science, New York, 1999, pp. 217-221.
11 True, W.R., “Weather, construction infla tion could squeeze North American pipelines,” Oil & Gas Journal , Aug. 31, 1998.

12 "Evaluation of Innovative Foss il Fuel Power Plants with CO 2 

### Removal," EPRI Report No. 1000316, Interim Report, December 2000, Cosponsors: U.S. Department of Energy/Office of Fossil Energy and U.S. Department of En ergy/NETL, EPRI Project Manager N. A. H. Holt. Available HTTP: http://www.netl.doe.gov/coalpow er/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 Ch emical 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 fu el fired power stations,” IEA Greenhouse R&D Programme, Cheltenha m. Tech. Rep. IEAGHG/SR3, Jun. 1994.
19 Summerfield, I.R., S.H. Goldthorpe, N. Williams and A. Sheikh, “Costs of CO 2 

### disposal options,” Energy Convers. Mgmt , vol. 34, no. 9-11, pp. 1105-1112, 1993.

### 5

- 1 

# 5 ENHANCED OIL RECOVER Y

5.1 Introduction
This chapter deals with the storage of CO 2 

### in depleted oil reservoirs where enhanced oil production results in a value

- added product. The use of depleted oil reservoirs for CO 2 

### 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 CO 2 

### floods (CO 2 

### - EOR) worldwide in 2000. The amount of enhanced oil production from these CO 2 

### - EOR projects during that year averaged 200,772 barrels (bbl) of oil per day a 

### , 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 t he 84 projects, or 96 percent of worldwide enhanced oil production from CO 2 

### floods, and is as such the world leader in the use of CO 2 

### - EOR technology. Currently, Turkey is the only other country with a commercial

scale application of CO 2 

### - EOR, with Canada a nd Trinidad having only pilot

- scale projects. 1 , 2


### Enhanced oil production from the 72 CO 2 

### 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 thes e CO 2 

### 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 CO 2 

### floods in the United States are in the Rocky Mountain and Mid

- continent regions. The details of the six largest CO 2 

### - 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 CO 2 

### floods. 1 , 2

a Only the oil recovered due to the CO 2 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 CO 2 - EOR projects 1

Operator Field Basin/Region Area (km 2 ) Production

Wells Injection

Wells EOR

Production (bbl/d)
Altura Wasson

(Denver) Permian 113 735 385 29,000

Amer ada 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



### C urrently, there is no commercial

- scale CO 2 

### - EOR project that utilizes CO 2 

### from a power plant. In the 1980s, there were three small

- scale CO 2 

### - EOR projects that utilized CO 2 

### from gas boiler power plants. These plants were shut down when the price of oil drop ped in the mid

- 1980s, making this source of CO 2 

### too expensive. 3 

### The main obstacle to the utilization of this source of CO 2 

### for EOR is its significant cost of capture. Most of the CO 2 

### - EOR projects, particularly those located in the Permian basin, are depen dent upon naturally occurring CO 2 

### , which is obtained from high

- pressure, high

- purity underground deposits. The most important of these natural CO 2 

### 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 CO 2 

### 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 regio ns are almost wholly supplied by anthropogenic CO 2 

### from gas processing and fertilizer production facilities. The Rangely Weber Sand CO 2 

### - EOR project, for example, is supplied by the La Barge gas processing plant in southwestern Wyoming and is as such the w orld’s largest single sequestration site of anthropogenic CO 2 

### . 5



### 5.2.2 Storage Potential The Weyburn Field in southeastern Saskatchewan, Canada, is the only CO 2 

### - EOR project to date that has been monitored specifically to understand CO 2 

### sequestration. In th e case of most CO 2 

### - EOR projects, much of the CO 2 

### 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 CO 2 

### being released b 

### , with a small but significant amount of the injected CO 2 

### remaining

b The CO 2 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 allowin g for permanent CO 2 

### sequestration. Over the anticipated 25

- year life of the project, it is expected that the injection of some 18 million tonnes of CO 2 

### from the Dakota Gasification Facility in North Dakota will produce around 130 million bbl of enhanced o il. This has been calculated to be equivalent to, taking into account the CO 2 

### emitted by the generation of electricity required, approximately 14 million tonnes of CO 2 

### being prevented from reaching the atmosphere. 5 , 6



### 5.2.3 Storage Mechanics

Most CO 2 

### flood s achieve enhanced oil production through miscible, as opposed to immiscible, displacement. The six largest CO 2 

### - EOR projects described above, for example, are all miscible CO 2 

### floods. Miscible displacement involves the injected CO 2 

### mixing thoroughly with the oil in the reservoir whereas, in the case of immiscible displacement, the CO 2 

### 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 grea ter 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 recover ies compared to miscible conditions, but can still achieve a high recovery rate due to oil swelling and viscosity reduction. Currently, only one large CO 2 

### - EOR project, located in Turkey, utilizes immiscible processes. However, it is expected that the num ber of immiscible CO 2 

### floods will increase as the use of CO 2 

### - EOR becomes increasingly widespread. 1 , 5 , 7 , 8 , 9



### In CO 2 

### - EOR projects, it is most common for the CO 2 

### 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 CO 2 

### mobility that greatly reduces the effectiveness of CO 2 

### flooding. This high CO 2 

### mobility problem, caused by the CO 2 

### 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 CO 2 

### flowing through areas that have already been swept. Taking advantage of the fact t hat water is less mobile than CO 2 

### , the WAG process is able to significantly improve the sweep efficiency through reducing CO 2 

### mobility. This, in turn, results in improved oil recovery while also preventing early CO 2 

### breakthrough in producing wells. The w orld’s largest CO 2 

### - EOR project, Wasson (Denver), is an example of a WAG flood. 5 , 9 , 10



### 5.2.4 Feasibility of Storage Option

The use of CO 2 

### floods for EOR presents a very attractive CO 2 

### storage option. Even without CO 2 

### sequestration credits, most of the active CO 2 

### - EOR projects are profitable. In addition to a value

added product, CO 2 

### - 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 CO 2 

### flood pe rformance. This CO 2 

### storage option also has the added bonus that most oil fields have already undergone primary and secondary recovery prior to CO 2 

### flooding. This means that certain components of the existing infrastructure, such as the wells, are able t o be simply adaptedEnhanced Oil Recovery



### 5

- 4 for CO 2 

### storage purposes. There is the downside that CO 2 

### floods require significant additional infrastructure to handle the processing and recycling of CO 2 

### . On a positive note, however, the cost of anticorrosive equipment to deal with the problem of CO 2 

### reacting with water to form carbonic acid has recently been reduced. 5 , 7



## 5.3 Process Description

The CO 2 

### for the EOR case is taken from Case 3a of the DOE/EPRI Report on CO 2 

### removal from fossil fuel power plants. 11 

### This case is used for the design basis since potential CO 2 

### sources from a coal

- based power plant would most probably be associated with an IGCC plant. CO 2 

### recovery from IGCC is most economical because of the CO 2 

### 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 CO 2 

### per day. It is important to note that, since the capacity factor of the IGCC power plant is assumed to be 80 percent, this n ew CO 2 

### 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 CO 2 

### from the IGCC power plant to the EOR field. Fi rst, the CO 2 

### 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 CO 2 

### a distance of 100 km to the EOR field, where it is mixed with recycled CO 2 

### and inje cted into the EOR CO 2 

### injection wells. Third, the oil produced at the EOR wells is separated from water and CO 2 

### at the surface. Finally, the CO 2 

### is dehydrated, compressed, and mixed with fresh incoming CO 2 

### . The CO 2 

### injection wells are an important compon ent of the EOR field. These wells function as conduits for moving supercritical CO 2 

### 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 Dri

nking 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 CO 2 

### from the pipeline terminal and distributes it to the EOR CO 2 

### injection wells.

• Gathers oil from the EOR production wells and delivers it to the tank battery.

• Compresses separated CO 2 

### and mixes it with pipeline CO 2 

### for injection into EOR CO 2 

### 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 t his case CO 2 

### ), salt water, and sediment. First, most of the CO 2 

### 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 mi xture is accomplished in special tanks where the settling processEnhanced 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 CO 2 

### , which is also recompressed and recycled.

Testin g 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 CO 2 

### - EOR projects take place at fields that have already undergone secondary recovery, i.e. waterfl ooding. The modification of water

- flooded fields for CO 2 

### flooding involves:

• Makeover and equipping of injection wells

• Installation of CO 2 

### distribution and recycle systems

• Provision of high

- pressure injection equipment and related piping

• Replacement of sel

ected production facilities The production phase of the Weyburn field is expected to be more than 25 years. The Millennium Energy CO 2 

### 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 CO 2 

### Injection Wells

20 kW 

### Free Water Separator

Sales Oil 400 barrels/day 

### Recycle Compressor 240 kW ( avg )

150 tonne /day ( avg ) 103 bar

CO 2

Water



### Water Injection Well 10/11 Well EOR Module Total CO 2 Flow 

### CO 2 

### Pipeline Terminal 152 bar



### Boost Compressor 2,650 kW

100 km Pipeline, 12 ” diameter 7,389 tonne /day 83 bar 

### IGCC Plant CO 2 

### Source



## Block Flow Diagram: EOR 134 tonne /day

Figure 5
- 1 EOR block flow diagramEnhanced Oil Recovery



### 5

- 7



## 5.4 Methodology Used
For specific projects, the complex inte ractions between the injected CO 2 

### and reservoir oil would be modeled to assess the likely performance of a proposed CO 2 

### - EOR project. Based on the output of this modeling, the cost of the proposed CO 2 

### 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 CO 2 

### - 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 CO 2 

### mass flow rate is determined using a CO 2 

### effectiveness factor of 170 standard cubic meters (6 ,000 standard cubic feet) of new CO 2 

### 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 wel l. 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 CO 2 

### recycle plant is determined based on a maximum CO 2 

### recycle ratio of 3, wit h 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 e ach of these steps being discussed below in more detail.

5.4.1 CO

2 

### Effectiveness

For the EOR

- design basis, an average of 170 scm (6,000 scf) of CO 2 

### is taken to remain in the ground for each bbl of enhanced oil production. It is important to note, however, that the effectiveness of CO 2 

### - 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 CO 2 

### per bbl of enhanced oil would remain in the ground. 13 

### In contrast, the CO 2 

### 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 CO 2 

### 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
s cf) of CO 2 

### per bbl of enhanced oil was chosen for the sensitivity calculation.

The CO 2 

### - EOR projects in Table 5

- 2 have illustrate the range of CO 2 

### effectiveness. The projects chosen include two of the largest CO 2 

### floods in the Permian basin. In addition, two other smaller CO 2 

### - EOR projects in this basin, namely Dollarhide (Devonian) and Vacuum, are provided as examples of CO 2 

### floods displaying relatively high and low CO 2 

### effectiveness, respectively. CO 2 

### - EOR projects located in the other two main CO 2 

### - flood regions are also included. These projects comprise the two largest CO 2 

### floods in the Rocky Mountain region while, for the Mid

continent region, data was only available for two medium

- sized floods. Finally, a last case study is made of the highly efficien t CO 2 

### 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 maintenanc e, subsurface maintenance Inputs: Well depth Capital charge rate Outputs: Total capital cost Total O&M cost Total annual cost Total cost per tonne CO 2 

### Input: CO 2 

### mass flow rate ENHANCED OIL

PRODUCTION CALCU LATION

Total enhanced oil production

PRODUCTION WELL NUMBER CALCULATION INJECTION WELL NUMBER CALCULATION

Number of production wells QUANTITY CO 2 

### RECYCLED CALCULATION Rule of thumb: CO 2 

### effectiveness Rule of thumb: Rate of enhanced oil production at producer Rule of thumb: Ratio of producers to injectors Rule of thumb: CO 2 

### recycle ratio

Number of 10/11 well modules Input: CO 2 

### mass flow rate

Compressor size and power requirement

Figure 5
- 2 EOR cost model overview diagramEnhanced Oil Recovery



### 5

- 9

Table 5
- 2 Estimated CO 2 effectiveness for selected CO 2 - EOR projects 5 , 6

Operator F ield Basin/Region Est. Ultimate EOR (million bbl) Est. Ultimate CO 2

Sequestered (giga scm) Est. CO 2

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 Do llarhide

(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 CO 2 

### effectiveness has been calculated for the above CO 2 

### - EOR projects by dividing the estimated total amount of CO 2 

### to be sequestered, taken as being equal to 90 percent of the CO 2 

### purchased, 5 

### by the estimated total amount of enhanced oil to be recovered over the lifetime of the project. The resulting estimates of CO 2 

### effectiveness are all, except for those for the Sho

- Vel

Tum and Weyburn Field CO 2 

### floods, within the selected range of 85 to 227 scm (3,000 to 8 ,000 scf) of CO 2 

### per bbl of enhanced oil. In the case of the Sho

- Vel

- Tum flood, the use of the less

efficient immiscible displacement process to recover enhanced oil is the likely cause of the exceedingly high CO 2 

### effectiveness value. 1



### 5.4.2 Rate of Enhan ced 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 CO 2 

### - 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 CO 2 

### flood is located. The values of average daily enhanced oil production per well for the six largest CO 2 

### - 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 CO 2 - EOR projects 1

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 km 2 

### (20 acres), while in another it might be one well per 1.30 km 2 

### (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 Produce rs 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 ‘r ule 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 CO 2 

### - EOR projects. It can be s een 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 CO 2 

### is injected continuously. The Lost Soldier (Tenslee p) CO 2 

### - EOR project, the second largest in the Rocky Mountain region, is also included in the table as it provides an example of a CO 2 

### flood having a producer to injector ratio of 1 to 1.1.Enhanced Oil Recovery



### 5

- 11

Table 5
- 4 Ratio of p roducers to injectors for selected CO 2 - EOR projects 5 , 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

(Tensle ep) Rocky

Mountain WAG 54 60 1:1.1



### 5.4.4 CO

2 

### Recycle Ratio

The CO 2 

### recycle ratio is taken to have an average value of 1.1. 15 

### This value is used to calculate the power requirements of the CO 2 

### recycle plant. The CO 2 

### recycle ratio increases over the lifeti me of the CO 2 

### flood, from effectively zero to its maximum value, as the amount of CO 2 

### 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 CO 2 

### - EOR project. During the first 10 years of CO 2 

### flooding, 9 giga scm of net CO 2 

### purchases and 10 giga scm of recycled CO 2 

### 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 CO 2 

### purchases and 3.3 million scm per day of recycled CO 2 

### 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 sec ondary flooding and that the CO 2 

### 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 a re 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. Al so, it is necessary to provide the appropriate injection surface facilities. 13 , 14Enhanced 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’ report 16 

### includes a scenario for secondary oil recovery using water flooding. Costs and in dices 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 n ominally 4,000 feet, or 1,219 m, deep. This scenario was modified for CO 2 

### 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 $/mod ule 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 M aintenance (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 Se rvices $/module 15,100 Equipment Repair $/module 11,200 Other $/module 9,900Enhanced 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 Operati ons’ report 16 

### 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 dat

a 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 le ase 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. Th e 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 CO 2 

### effectiveness factor of 170 scm (6,000 scf) per bb l 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 CO 2 

### per day, the total enhanced oil production is calculated using the CO 2 

### 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 d esign 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 CO 2 

### and CO 2 

### to be recycled, assuming a maximum recycle ratio of 3, per module are 68,000 and 204,00 0 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
scf/bbl enhanced oil 6,000 CO 2 Effectiveness
scm/bbl enh anced 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 CO 2 * scm/d/module 68,000
Maxi mum Recycled CO 2 * 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 ye ars of operation, it is assumed that the ratio of CO 2 

### produced with the enhanced oil production to the new CO 2 

### will reach the maximum value of 3.
The compressor is sized to handle all of the CO 2 

### that is recycled in the 10/11 well module. The actual compre ssor is a Superior Model WG74, sized by Cooper Energy Services and priced by Gas Packagers, Inc. To meet the CO 2 

### recycle requirements of the 10/11 well module, two compressors, each delivering 71 scm per minute are required. The base cost for each compre ssor 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 c ost

Parameter Unit Individual Compressor Total for 10/11 Module
Maximum CO 2 to be Compressed scm/d 102,000 204,000 Compressor Type Reciprocating Reciprocating
bar 7 7 Suction Pressure psia 100 100
bar 103 103 Discharge Pressure psia 1,500 1,500
C ompressor 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/thous and 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
M anifold 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
W ater 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, po wer 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 Distribu tion 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 & Se rvices): 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 $27,000,000Enhanced Oil Recovery



### 5

- 19



## 5.6 Model Results
This section presents costs for CO 2 

### 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 monitor ing 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. Appe ndix 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% CO 2 Capture Cost ($/tonne captured) $14.55 Pipeline Distance 100 km COE Including CO 2 Capture Cost ($/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) $13.13 Well Depth (ft) 4,000 Break Even Oil Price to Offset CO 2 Storage Costs ($/bbl) CO 2 Effectiveness (scm/bbl) 170 Years 1-20 $10.6360 CO 2 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 & CO 2 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 Sum mary 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 CO 2 Stored (M tonne/yr) 2.158 Capital O&M EOR Well Modules 56 CO 2 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 $28,500,000 Break Even Oil Price to Offset CO 2 Storage Costs ($/bbl) Years 1-20 Years 1-100, NPV Basis

1. Levelized Annual CO 2 Storage Costs (years 1-20) CO 2 Captured Basis LC Avoided GHG Basis (storage only) CO 2 Storage Cost, M $/yr 60.17 60.17 Enhanced Oil Revenues, M $/yr 84.86 84.86 Net CO 2 Storage Cost, M $/yr (24.69) (24.69) CO 2 Stored or Avoided, M tonne/yr 2.16 2.02 Net Storage Cost, $/tonne CO 2 (11.44) (12.21)

2. Levelized Annual CO 2 Capture and Net Storage Costs (years 1-20) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

CO 2 Capture Cost, M $/yr 31.39 31.39 31.39 Net CO 2 Storage Cost, M $/yr (24.69) (24.69) (24.69) Capture + Net Storage Cost, M$/yr 6.70 6.70 6.70 CO 2 Stored or Avoided, M tonne/yr 2.16 1.69 1.67 Capture + Net Storage Cost, $/tonne CO 2 3.11 3.96 4.01

3. CO 2 Capture and Net Storage Costs, NPV Basis (years 1-100) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

NPV of CO 2 Capture Costs, M$ 357.42 357.42 357.42 NPV of Net CO 2 Storage Costs, M$ (280.04) (280.04) (280.04) NPV of Capture + Net Storage Costs, M$ 77.38 77.38 77.38 NPV of CO 2 Stored or Avoided, M tonne 24.57 19.29 19.04 Capture + Net Storage Cost (NPV Basis), $/tonne CO 2 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 CO

2 Capture & Storage $15.00



## 5.7 Sensit ivity Analysis
The sensitivity of the CO 2 

### 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, CO 2 

### effectiveness, recycle ratio and pipel ine 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 CO 2 

### 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 CO 2 

### 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


### Hig h 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 val ues 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 th at 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 CO 2 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 CO 2 

### storage cost for EOR can be see n to be widely different for the high and low cost cases. In reality, a CO 2 

### - 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 CO 2 scm/d/module 68,000 45,000 59,000
Maximum Re cycled CO 2 scm/d/module 204,000 182,000 59,000 Levelized Annual Net CO 2 Storage Cost $/tonne CO 2 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 th e 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 functions 15

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
Work over of production surface facilities $/well 10,000
CO 2 recycle plant $ 457,000 * CO 2 recycled (million scf per day)
O&M COSTS
CO 2 recycle compression operating costs $/y 200 * CO 2 recycled (million scf per day) * 365Enhanced Oil Recovery



### 5

- 23

Using these cost functions, the c apital 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 E

OR cost results 15 , 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
Workov er 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
CO 2 recycle plant $ 105,660,000 182,354,000 Total Capital Cost $ 182,800,000 256,947,000

O& M COSTS
CO 2 recycle compression operating costs $/yr 12,232,000 14,540,000

TOTAL O&M COSTS $/yr 27,000,000
-



### Except for the CO 2 

### 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 c ost data. The CO 2 

### 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 Her zog, H., “An introduction to CO 2 

### separation and capture technologies,” Energy Laboratory Working Paper, 1999.Enhanced Oil Recovery



### 5

- 24 4 Kinder Morgan CO 2 

### Company, “CO 2 

### Supply,” [Online document], 2001, [cited Jan. 2002], Available HTTP: http://www.kne.com/co2/supply.cfm

5 “E nhanced 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 CO 2 

### 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 CO 2 

### flooding?,” Center for Energy and Economic Diversification (CEED), Odessa, TX. C EED CO 2 

### 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.do e.gov/oil_gas/natural_gas/data_publications/cost_indices/c_i.html

### 6

- 1 

# 6 ENHANCED COALBED MET HANE

6.1 Introduction
This chapter looks at the injection of CO 2 

### into deep coal seams as a means of enhancing coalbed methane production while simultaneously sequestering CO 2 

### .


## 6.2 State of the Art

6.2.1 Applications
The injection of CO 2 

### into deep, unmineable coal seams to enhance coal

- bed methane production (CO 2 

### - ECBMR) is a relatively recent technology. It was not until 1996 that the world’s first, and to date only, pilot

- scale application of CO 2 

### - ECBMR began operation. In contrast, EO R using CO 2 

### floods (CO 2 

### - EOR) is a mature technology with over three decades of commercial

- scale application. The one CO 2 

### - ECBMR project, comprising nine coal

- bed methane (CBM) production and four CO 2 

### injection wells, is located in the southwestern United S tates 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 CO 2 

### - ECBMR process to be technically a nd 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 coa l. 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 compari son, over 90 percent of the OGIP can theoretically be recovered using CO 2 

### - ECBMR.Enhanced Coal b ed Methane



### 6

- 2 Furthermore, CO 2 

### - ECBMR can accelerate CBM recovery, providing greater real value for a given reserve. 1 , 2


### Significant potential for CO 2 

### - ECBMR exists worldwide. In order for CO 2 

### - 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 CO 2 

### - ECBMR potential. Indeed, the total worldwide potential for CO 2 

### - ECBMR, taking into consideration only those reservoirs where CO 2 

### - ECBMR could b e profitably developed without CO 2 

### sequestration credits or free or reduced

- cost CO 2 

### supplies, is estimated at around two trillion scm of CBM, with about 7. 1 billion tonnes of associated CO 2 

### sequestration potential. 5



### 6.2.3 Storage Mechanics
Four patents ha ve been issued over the past two decades relating to the CO 2 

### - ECBMR process. Each of these patents is based on the principle that CO 2 

### is adsorbed more readily onto the coal matrix than methane. Specifically, CO 2 

### - ECBMR involves injected CO 2 

### 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 CO 2 

### are required to displace one unit volume of methane. This ra tio of CO 2 

### effectiveness is however expected to vary in the field according to the thermal maturity of the coal. 1 , 2 , 5 , 6



### A successful demo nstration of CO 2 

### - ECBMR technology has been provided by the Allison unit pilot project. Prior to CO 2 

### injection, CBM was produced within the unit using conventional pressure

- depletion methods. Since the start of CO 2 

### injection, enhanced CBM production has b een 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 CO 2 

### breakthrough, despite around on e billion scf of CO 2 

### being injected each year since the project began in 1996. The injected CO 2 

### comes from the McElmo Dome, which is a natural CO 2 

### deposit in southwestern Colorado. 1 , 2 , 3



### 6.2.4 Feasibility of Storage Option

CO 2 

### - ECBMR presents an attractive option for the sequestering of CO 2 

### . Like CO 2 

### - EOR, it has the distinct advantage over other CO 2 

### storage options that it s equesters CO 2 

### while also generating a value

- added product. While CO 2 

### - 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 simila rities between the two, the technology required to implement CO 2 

### - ECBMR in the field can be largely based on that used for CO 2 

### - EOR operations. The fact that CO 2 

### is adsorbed onto the coal surface means that there should be little risk of leakage of CO 2 

### from the reservoir. Also on a positive note, coal, and so CBM, typicallyEnhanced Coal b ed Meth ane



### 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, CO 2 

### - ECBMR is a very energy intensive pr ocess, 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 fo rmation water produced by CO 2 

### - 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 CO 2 

### from the IGCC plan

t to the ECBMR field. First, the CO 2 

### 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 CO 2 

### a distance of 100 km to the ECBMR field, where it is injec ted into the ECBMR CO 2 

### 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 sou rce and quantity of CO 2 

### 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 vertica l 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 (assumi ng 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 invol ves:

• 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 E CBMR 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 CO 2 

### from the pipeline terminal and dis tributes it to the ECBMR CO 2 

### injection wells.Enhanced Coal b ed 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 wa ter 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, w

ater 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 plan t, 4 years.Enhanced Coal b ed Meth ane



### 6

- 5 10 Producing Wells 90 kW 

### 10 CO 2 

### Injection Wells

20 kW 

### Inlet Separator

Free Water Separator Gathering Compressor 4.5 bar

157 kW CO 2

Water



### Water Injection Well 10/10 Well ECBM Module Total CO 2 Flow 

### CO 2 

### 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 2.4 bar 140 Mscmd 

### Gathering Line 1.88 MMscmd Sales Gas

25 bar 5,655 kW

Figure 6
- 1 

### ECBMR block flow diagramEnhanced Coal b ed Methane



### 6

- 6 

## 6.4 Methodology Used

As in the case of the CO 2 

### - EOR concept design, ‘rules of thumb’ are used to define the engineering parameters needed to estimate the cost of a CO 2 

### - ECBMR project. As for CO 2 

### - EOR, the method for costing the CO 2 

### - ECBMR process is also split up into a number of steps. First, the total amount of enhanced CBM produced per day for the given CO 2 

### mass flow rate is determined using a CO 2 

### effectivene ss factor of two scm CO 2 

### 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 CO 2 

### is required. Finally, the cost of drilling and equipping the required production an d injection wells is calculated. An overview of the cost model is provided in Figure 6

- 2.

6.4.1 CO

2 

### Effectiveness For the design basis, it is assumed that 2 scm of CO 2 

### needs to be injected to produce 1 scm of enhanced CBM. This CO 2 

### 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 CO 2 

### by volume as methane. This may vary, however, as a result of other physi cal processes active within a coal reservoir. Based on data collected in the field, one source 11 

### has reported the amount of CO 2 

### injected to CBM produced as being between 1.5 and 2 while another 12 

### 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 CO 2 

### 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 b e 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 Coal b ed Meth ane



### 6

- 7 COST MODEL Internal Calcs: Capital costs: Front end, lease acquisition, injection equipment, producing equipment, production & i njection 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 CO 2 

### Input: CO 2 

### mass flow rate ENHANCED CBM

PRODUCTION CALCULATION

Total enhanced CBM production PRODUCTION WELL NUMBER CALCULATION INJECTION WEL L NUMBER CALCULATION

Number of production wells Rule of thumb: CO 2 

### 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 diagramEnhanced 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 CO 2 

### injection, the #115 well had been a sub

- average performer, with a CBM production rate of 14,000 scm per day. However, following CO 2 

### injection, the daily CBM production rate rose sharply to 37,000 scm. 2 , 13


### Based on the values given above for projects without CO 2 

### injection as well as the Allison unit CO 2 

### - ECBMR

pilot project, and advice from experts in the field 11 , 12 

### , the assumed base

- case value of 14,000 scm of enhanced CBM per day per well was chosen. A sensitivity anal ysis 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 CO 2 

### - ECBMR potential.

6.4.3 Ratio of Producers to Injectors A ratio of producers to injectors of 1 to 1 is assumed for the CO 2 

### - 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 u

sed in the case of the Allison unit CO 2 

### - ECBMR pilot project, which comprises nine CBM production and four CO 2 

### 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 CO 2 

### - ECBMR concept design, which comprises a relatively large nu mber of wells, a ratio of producers to injectors of 1 to 1 is therefore used.

6.4.4 CO

2 

### Recycle Ratio

The CO 2 

### - ECBMR concept design assumes that CO 2 

### breakthrough at the production wells is negligible and that there is, therefore, no need for CO 2 

### recycling. At the Allison unit, breakthrough of CO 2 

### has been minimal during the life of the project. Following almost five years of injection, the CO 2 

### 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 CO 2 

### - ECBMR process is calculated based on both production and injection wells needing to be drilled and equipped. If a co al bed is viewed primarily as a source of CBM, it makes more economic sense to partially deplete the reservoir of CBM before injecting CO 2 

### . However, in the case that the primary role of the coal bed is as a repository for CO 2 

### , early use of CO 2 

### - ECBMR is fa vored. Given that the concern here is CO 2 

### sequestration, it is assumed for theEnhanced Coal b ed Meth ane



### 6

- 9 purpose of the concept design that no CBM production has taken place at the coal bed prior to CO 2 

### injection. This assumption implies that production and injection wells need t o 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 nor mal 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 req uired 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 ‘ Cost s and Indices for Domestic Oil and Gas Field Equipment and Production Operations’
r eport. 14 

### A representative ECBMR le ase, or module, comprising 10 CO 2 

### injection wells and 10 producing wells with dewatering facilities is used for the design basis. The 10 CO 2 

### injection wells are drilled to a depth of 610 m and equipped with a battery of lease equipment, which includes dis tribution 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 con tained 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 = 0.0888e 0.0008x

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 depthEnhanced 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 bas is.
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 S ervice $/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 D aily 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 0.044Enhanced Coal b ed Meth ane



### 6

- 11 Table 6
- 1 (continu ed) 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 $/m odule 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 clos ely as possible to the EIA ‘Costs and Indices for Domestic Oil and Gas Field Equipment and Production Operations’ report 14 

### 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 CO 2 

### 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 CO 2 

### per day, the total en hanced CBM production is calculated using the CO 2 

### 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 p roducer 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 E CBMR base case

Parameter Unit ECBMR Base Case
CO 2 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 CO 2 Wells* 135
New CO 2 * 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 comp ressed 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 Ra te thousand scm/d 140
bar 2.4 Suction Pressure psia 24.7
bar 4.5 Discharge Pressure 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 $ 105,000Enhanced Coal b ed Meth ane



### 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
bar 4.5 Suct ion Pressure psia 64.7
bar 25.1 Discharge Pressure 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, G rade 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 fe et, 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 c m, 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 capi tal and O&M costs for the base case EOR design.Enhanced Coal b ed Meth ane



### 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
Manif old $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
Su btotal $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) $572,000Enhanced 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 Equipme nt 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 CO 2 

### 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 i nclude 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 Tabl e 6

- 7.Enhanced Coal b ed Meth ane



### 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) $55.08 Pipeline Distance 62 miles Nominal pipe size (in) 12 CO 2 Storage Gas Royalty 12.5% CO 2 Storage Gas Price ($/10 6 BTU) $2.00 Well Depth (m) 610 Gas Credit ($/10 6 BTU) $1.75 Well Depth (ft) 2,000 Break Even Gas Price to Offset CO2 Storage Costs ($/10 6 BTU) CO 2 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 & CO 2 Storage After-Tax Discount Rate 6.09% Levelized Carrying Charge Factor 15.0% Enhanced Coal Bed Methane Input Variables

10 6 Btu = 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 jo ule ($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 CO 2 Stored (M tonne/yr) 2.158 Well Depth (m) 610 CO 2 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 $216,000 After-Tax Discount Rate 6.09% Gas Price ($/10 6 BTU) $2.00 Gas Credit ($/10 6 BTU) Total $94,300,000 $8,100,000 Break Even Gas Price to Offset CO 2 Storage Costs ($/10 6 BTU) Years 1-20 Years 1-100, NPV Basis

1. Levelized Annual CO 2 Storage Costs (years 1-20) CO 2 Captured Basis LC Avoided GHG Basis (storage only) CO 2 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) CO 2 Stored or Avoided, M tonne/yr 2.16 2.11 Net Storage Cost, $/tonne CO 2 (5.45) (5.59)

2. Levelized Annual CO 2 Capture and Net Storage Costs (years 1-20) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

CO 2 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 CO 2 Stored or Avoided, M tonne/yr 2.16 1.78 1.76 Capture + Net Storage Cost, $/tonne CO 2 9.10 11.06 11.18

3. CO 2 Capture and Net Storage Costs, NPV Basis (years 1-100) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

NPV of CO 2 Capture Costs, M$ 357.42 357.42 357.42 NPV of Net CO 2 Storage Costs, M$ (132.86) (132.86) (132.86) NPV of Capture + Net Storage Costs, M$ 224.56 224.56 224.56 NPV of CO 2 Stored or Avoided, M tonne 24.57 20.21 19.99 Capture + Net Storage Cost (NPV Basis), $/tonne CO 2 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 CO

2 Capture & Storage $1.75Enhanced Coal b ed Meth ane



### 6

- 19 

## 6.7 Sensitivity Analysis
The sensitivity of the CO 2 

### storage cost for ECBMR is determined for five key parameters: well depth, CO 2 

### effectiveness, CBM production rate, gas price and pipeline distance. It can be seen in Figure 6

- 4 that increases in well depth, CO 2 

### 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 CO 2 

### effectiveness.

ECBMR Cost Sensitivity Levelized Annual Net CO 2 

### 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 val ue 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

CO 2 Eff ectiveness 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 CO 2 

### storage cost for ECBMR can be seen to be widely different for the high and low cost cases. In reality, a CO 2 

### - ECBMR project with parameter values approachin g 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 Pro duction (million scm enhanced CBM/d) 1.88 0.38 2.51

Number of CBM Wells 135 126 84 Number of CO 2 Wells 135 126 84 New CO 2 scm/d/well 28,000 30,000 45,000 Levelized Annual Net CO 2 Storage Cost $/tonne CO 2 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 CO 2 

### - ECBMR base case design and those calculated using cost estimates from a paper by Wong, et al. 5 

### The costs for t hisEnhanced Coal b ed Meth ane



### 6

- 21 study were based on the EIA ‘Costs and Indices for Domestic Oil and Gas Field Equipment and Production Operations’ report 14 

### and the API ‘Joint Association Survey on Drilling Costs’ report 15 

### . Wong, et al., cost estimates are given for a conceptual 50

- well pair CO 2 

### - 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 CO 2 - 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 CO 2 

### - ECBMR were calculated for the 1,219 m case design. Table 6

- 12 shows the results of these calculations together with the previously determined CO 2 

### - ECBMR costs from this study. It should be noted that the latter, in order to make the results compara ble, 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 CO 2 - ECBMR cost results 5 , 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 COS TS
Well maintenance $/y 2,204,000 2,881,000 Subtotal $/y 7,660,000 not reported 

### This comparison shows that the cost of the CO 2 

### - 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 Stud y,” EPRI, Palo Alto, CA. Tech. Rep. 113836, 1999.

2 Stevens, S.H., D. Spector and P. Riemer, “Enhanced coalbed methane recovery using CO 2 

### injection: Worldwide resource and CO2 sequestration potential,” presented at the 1998 SPE International Conference a nd 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 Seques tration, 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.go v/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 CO 2 

### sequestration in coalbed methane reservoirs,” presented at the 2000 SPE/CERI Gas Technology Symposium, Calgary, Alber ta 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 CO 2 

### 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: Potentia l 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 communicatio n, Dec. 3, 2002.Enhanced Coal b ed Meth ane



### 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 S tatistics 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 CO 2 

### sequestration options considered here include CO 2 

### storage in depleted natural gas and oil reservoirs, and deep saline aquifers. Geologic CO 2 

### 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 CO 2 

### storage is in operation at the Sleipner West field. Sleipner West is a natural ga s/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 CO 2 

### 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 CO 2 

### to be vented to the atmosphere. At Sleipner, however, the CO 2 

### 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 CO 2 

### have been sequestered annually at Sleipner since October 1996, with a total of 20 million tonnes of CO 2 

### expected to be sequestered over the lifetime of the project. A second scheme, which would involve about 0.7 million tonnes per year of CO 2 

### 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 geologi c reservoir with significant potential for CO 2 

### sequestration is the abandoned natural gas field. Nearly all of the volume of abandoned gas fields should be available for CO 2 

### 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 manyDepleted Gas and Oil Reservoir/Aquifer Storage



### 7

- 2

years prior to CO 2 

### injection, the reservoir will be similar to an aquifer. It should be noted here that abando ned 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 b een proposed that CO 2 

### 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 CO 2 

### - 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, CO 2 

### injection into active gas fields is not considered here as a geologic CO 2 

### storage option. 1 , 6 , 7



### Depleted Oil Reservoirs The other type of hydrocarbon reservoir in which CO 2 

### 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 re sult 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 aqu

ifers have the greatest CO 2 

### 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 ver y 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 CO 2 

### injection wells be located in regions of high permeability and that the total amount of CO 2 

### 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 CO 2 

### , but this number is highly uncertain. 1


### To give an illustration of the aquifer volumes needed to store the CO 2 

### emissions from power plants, the aquifer volume required for the CO 2 

### captured at the base

- case IGCC plant was determined. Calculating the total quantity of CO 2 

### to be supplied by the base

- case plant over its 20

- year lifetime to be 43 Mt and assuming a supercritical CO 2 

### density of 0.7 kg/m 3 

### , it is found that an aquifer with an effective pore volume of approximately 0.0617 km 3 

### is needed. Given a realistic effec tive porosity of 30 percent and a storage efficiency of two percent, this is equivalent to a total aquifer volume of about 10.28 km 3 

### . This can be visualized as a circular

shaped aquifer with a diameter of 11.4 km and a thickness of 100 m. 1 , 8



### CO 2 

### 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 geolog ic trap. It should also be possible to inject the CO 2 

### into aquifers that do not have lateral seals. An impermeable caprock to prevent the buoyant CO 2 

### from escaping vertically and a down

- directed flow regime to transport the CO 2 

### away from the surface shou ld theoretically be sufficient to provide secure CO 2Depleted Gas and Oil Reservoir/Aquifer Storage



### 7

- 3

storage. 9 , 10 

### This possibility vastly increases the total potential CO 2 

### 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 CO 2 

### 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

CO 2 

### is stored in geologic formations by a number of different trapping mechanisms, with the exact mechanisms depending on the formation type. To make f ull use of storage capacity, the CO 2 

### 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 CO 2 

### is 740 kg/m 3 

### . Since the CO 2 

### under these pressure and temperature conditions will still be less dense than formation water, the CO 2 

### 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 CO 2 

### . In the case of aquifers with no distinct geologic traps, however, an impermeable caprock above the undergr ound reservoir is needed. This forces the CO 2 

### 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 CO 2 

### into fluids, and the reaction of CO 2 

### with minerals p resent 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 CO 2 

### moves through the reservoir along the flowpath , it comes into contact with uncarbonated formation water and reactive minerals. A proportion of the CO 2 

### dissolves in the formation water and some of this dissolved CO 2 

### becomes permanently fixed by reactions with minerals in the host rock. If the flowpat h is long enough, the CO 2 

### 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 CO 2 

### into geologic formations is a promising CO 2 

### sequestration option. First, the technology for injecting CO 2 

### 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 CO 2 

### for EOR fo r 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 CO 2 

### sequestration po tential associated with the injection of CO 2 

### into exhausted oil and natural gas reservoirs and deep saline aquifers greatly exceeds that of EOR and ECBMR using CO 2 

### 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 subsurfaceDepleted Gas and Oil Reservoir/Aquifer Storage



### 7

- 4 infrastructures could readily be converted for CO 2 

### distribution and injection. On the downside, the lo cating and sealing of abandoned wells could present an ongoing challenge. Aquifers, however, have a larger CO 2 

### capacity, are more widespread and are generally located closer to CO 2 

### 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 CO 2 

### from the IGC C plant to the depleted gas and oil fields, and aquifer. Developing a depleted reservoir or aquifer for CO 2 

### storage involves:

• Screening and evaluation of sites

• Drilling and equipping injection wells

• Installation of high

- pressure injection equipment and re lated piping Equipment must be available at the injection site to accept pressurized CO 2 

### 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 CO 2 

### to the injection wells, CO 2 

### flow controls equipment, and equipment to monitor well condition.
The source and quantity of CO 2 

### 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 to nnes) of CO 2 

### per day. Determining the required CO 2 

### pressure at the top of the well requires consideration of the pressure required at the bottom of the well to force CO 2 

### into the injection zone, the pressure increase in the pipe due to the height of the C O 2 

### column, and the pressure loss due to flow in the pipe. Moving the CO 2 

### into the reservoir requires raising the CO 2 

### 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 CO 2 

### injection wells are as described in Section 5.3. For geologic CO 2 

### storage, these wells are equipped with a battery of lease equipment that includes distribution lines, headers, electrical service and controls. This equipment enables the CO 2 

### 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 CO 2 

### Injection Wells CO 2 

### Pipeline Terminal 152 bar



### Boost Compressor 2,650 kW

100 km Pipeline, 12 ” diameter 83 bar 

### IGCC Plant CO 2 

### Source



## Block Flow Diagram: Depleted Oil & Gas, & Aquifer 7,389 tonne /day



### CO 2 

### Injection Wells 7,389 tonne

/day



## Aquifer Depleted Oil Reservoir CO 2 

### Injection Wells 7,389 tonne

/day



## Depleted Gas Reservoir

Figure 7
- 1 Depleted oil, gas, and aquifer block flow diagramDepleted 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 CO 2 

### 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 CO 2 

### storage options.
The cost model for the geologic CO 2 

### stora ge 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 CO 2 

### flow rate, CO 2 

### downhole injection pressure, and set of reservoir parameters. Second, an iterative proce dure is used to take into account the interdependent relationship between CO 2 

### 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 geo logical 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 CO 2 

### storage option. In order to take account of this variation, base and sensitivi ty 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 CO 2 viscosity CO 2 mobility CO 2 injectivity Injection rate per well

Num ber 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 CO 2 Inputs: CO 2 mass flow rate CO 2 surface injection pressure Reservoir pressure Reservoir thickness Reservoir depth Reservoi r permeability PRESSURE CHANGE CALCULATION Internal Calcs: Well diameter Gravity head Friction loss Pressure change CO 2 downhole injection pressureDepleted Gas and Oil Reservoir/Aquifer Storage



### 7

- 7

7.4.1 Well Number Calculation
The well number calculation requires inputs for CO 2 

### mass flow r ate, CO 2 

### 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 25 o 

### C/km. The visco sity of the CO 2 

### (μ CO2 

### ) is then calculated based on a correlation published by McHugh and Krukonis. 13 

### Next, the absolute permeability (k a 

### ) is found from

k a 

### = (k h 

### x k v 

### ) 0.5



### where k

v 

### = the vertical permeability and is equal to 0.3 times the horizontal permeability and k h 

### = t he given horizontal permeability. 14


### A relationship, derived by Law and Bachu, 14 

### is used to determine CO 2 

### injectivity from CO 2 

### mobility. This relationship is shown in Figure 7

- 3. The equation for CO 2 

### injectivity is

CO 2 

### injectivity = 0.0208 x CO 2 

### mobility

where CO 2 

### injectivity is equal to the mass flow rate of CO 2 

### (m) that can be injected per unit of reservoir thickness (h) and per unit of downhole pressure difference (P inj


### –P res 

### ), and CO 2 

### mobility equals the CO 2 

### absolute permeability (k a 

### ) divided by the CO 2 

### viscosity (μ CO2 

### ). Given the CO 2 

### injectivity, the CO 2 

### injection rate per well (Q CO2/well 

### ) can be found from
Q CO2/well 

### = CO 2 

### injectivity x h x (P inj


### –P res 

### )
Finally, the number of wells required (n) is given by
n = m/Q CO2/wellDepleted Gas and Oil Reservoir/Aquifer Storage



### 7

- 8 CO 2 

### Injectivity as a Function of CO 2 

### Mobility

y = 0.0208x

0.10 1.00 10.00 100.00

10 100 1000 10000 CO 2 

### mobility (md/mPa.s)CO2injectivity (t/d/m/MPa)

Figure 7
- 3 CO 2 injectivity as a function of CO 2 mobility



### 7.4.2 CO

2 

### Downhole Injection Pressure Calculation

The surface CO 2 

### injection pressure is equal to the 10. 3 MPa minimum CO 2 

### pipeline outlet pressure used in the design of the pipeline for CO 2 

### transport. Based on this value, no additional recompression of CO 2 

### is required at the wellhead. A typical well diameter of 0.059 m is used for the injection pipe.

An it erative procedure is used to calculate the CO 2 

### 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 i ncrease 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 CO 2 

### in each well. Well number, on the other hand, is determined by CO 2 

### injectivity, which i s 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 smal ler 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 s ite 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 accou

nt 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
No rmal 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 calc ulated 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 an alysis 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 = 0.0888e 0.0008x



### 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 fo r, 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
reservo irs 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

- 19Depleted 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
we re 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

- 3 2 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 a quifer 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 Aqu ifer 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 Ang eles 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 Sa ndstone, 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 ca ses 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 magnitud e. The base values for aquifer pressure and depth were based on the arithmetic mean. The high

cost 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, high

cost 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 storag e cost. A higher reservoir pressu re results in lower injectivity (not desired), but higher CO 2 

### densities (desired). Therefore, a moderate pressure is optimal. In general, the permeability is the most crit ical parameter in determining costs.
It is important to note that the inje ction rate per well of 889,495 tonnes of CO 2 

### per day for the low cost case was calculated without setting any limit on the well diameter. Indeed, this injection rate requires a well diamet er of 0.5 m, which is too large to be used in practice. Given that the standard well diameter used for the ot her 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 CO 2 

### per day.

7.5.2 Capital and O&M Cost Inputs
The capital cost of site screening and evaluati on is assumed equal ( $1,685,000) for each of the three geologic storage options. The other capita l 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 S ection 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 th e 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 (Re pair & Services) $300,000 $134,000 $5,000

Subtotal $1,970,000 $970,000 $100,000



## 7.6 Model Results
This section presents costs for CO 2 

### capture and storage for the base cases described in Section 7.5 . The storage costs comprise transaction, transportati on, 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% CO 2 Capture Cost ($/tonne captured) $14.55 Pipeline Distance 100 km COE Including CO 2 Capture Cost ($/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 CO 2 Stored (M tonne/yr) 2.158 Capital O&M Design Basis: Base Cost Case CO 2 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 CO 2 Storage Costs (years 1-20) CO 2 Captured Basis LC Avoided GHG Basis (storage only) CO 2 Storage Cost, M $/yr 10.48 10.48 CO 2 Stored or Avoided, M tonne/yr 2.16 2.15 Storage Cost, $/tonne CO 2 4.86 4.87

2. Levelized Annual CO 2 Capture and Storage Costs (years 1-20) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

CO 2 Capture Cost, M $/yr 31.39 31.39 31.39 CO 2 Storage Cost, M $/yr 10.48 10.48 10.48 Capture + Storage Cost, M$/yr 41.87 41.87 41.87 CO 2 Stored or Avoided, M tonne/yr 2.16 1.82 1.80 Capture + Storage Cost, $/tonne CO 2 19.41 22.98 23.21

3. CO 2 Capture and Net Storage Costs, NPV Basis (years 1-100) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

NPV of CO 2 Capture Costs, M$ 357.42 357.42 357.42 NPV of CO 2 Storage Costs, M$ 120.45 120.45 120.45 NPV of Capture + Storage Costs, M$ 477.87 477.87 477.87 NPV of CO 2 Stored or Avoided, M tonne 24.57 20.75 20.55 Capture + Storage Cost (NPV Basis), $/tonne CO 2 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 CO

2 Capture & Storage



### 7.6.2 Depleted Oil Reservoir: Spreadshee t 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 Summar y 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% CO 2 Capture Cost ($/tonne captured) $14.55 Pipeline Distance 100 km COE Including CO 2 Capture Cost ($/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 VariablesDepleted 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 CO 2 Stored (M tonne/yr) 2.158 Capital O&M Design Basis: Base Cost Case CO 2 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 CO 2 Storage Costs (years 1-20) CO 2 Captured Basis LC Avoided GHG Basis (storage only) CO 2 Storage Cost, M $/yr 8.22 8.22 CO 2 Stored or Avoided, M tonne/yr 2.16 2.15 Storage Cost, $/tonne CO 2 3.81 3.82

2. Levelized Annual CO 2 Capture and Storage Costs (years 1-20) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

CO 2 Capture Cost, M $/yr 31.39 31.39 31.39 CO 2 Storage Cost, M $/yr 8.22 8.22 8.22 Capture + Storage Cost, M$/yr 39.61 39.61 39.61 CO 2 Stored or Avoided, M tonne/yr 2.16 1.82 1.80 Capture + Storage Cost, $/tonne CO 2 18.36 21.74 21.95

3. CO 2 Capture and Storage Costs, NPV Basis (years 1-100) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

NPV of CO 2 Capture Costs, M$ 357.42 357.42 357.42 NPV of CO 2 Storage Costs, M$ 94.71 94.71 94.71 NPV of Capture + Storage Costs, M$ 452.14 452.14 452.14 NPV of CO 2 Stored or Avoided, M tonne 24.57 20.75 20.55 Capture + Storage Cost (NPV Basis), $/tonne CO 2 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 CO 2

Capture & StorageDepleted 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% CO 2 Capture Cost ($/tonne captured) $14.55 Pipeline Distance 100 km COE Including CO 2 Capture Cost ($/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) 1,239 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 presen ted 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 CO 2 Stored (M tonne/yr) 2.158 Capital O&M Design Basis: Base Cost Case CO 2 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 CO 2 Storage Costs (years 1-20) CO 2 Captured Basis LC Avoided GHG Basis (storage only) CO 2 Storage Cost, M $/yr 6.41 6.41 CO 2 Stored or Avoided, M tonne/yr 2.16 2.15 Storage Cost, $/tonne CO 2 2.97 2.98

2. Levelized Annual CO 2 Capture and Storage Costs (years 1-20) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

CO 2 Capture Cost, M $/yr 31.39 31.39 31.39 CO 2 Storage Cost, M $/yr 6.41 6.41 6.41 Capture + Storage Cost, M$/yr 37.80 37.80 37.80 CO 2 Stored or Avoided, M tonne/yr 2.16 1.82 1.80 Capture + Storage Cost, $/tonne CO 2 17.52 20.74 20.95

3. CO 2 Capture and Net Storage Costs, NPV Basis (years 1-100) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

NPV of CO 2 Capture Costs, M$ 357.42 357.42 357.42 NPV of CO 2 Storage Costs, M$ 74.08 74.08 74.08 NPV of Capture + Storage Costs, M$ 431.50 431.50 431.50 NPV of CO 2 Stored or Avoided, M tonne 24.57 20.75 20.55 Capture + Storage Cost (NPV Basis), $/tonne CO 2 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 CO

2 Capture & Storage



## 7.7 Sensitivity Analyses

7.7.1 Depleted Gas Reservoir The sensitivity of the CO 2 

### storage cost for the depleted gas reservoir o ption 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 CO 2 

### 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
r ange of CO 2 

### 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

P arameter 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 CO 2 Storage Cost $/tonne CO 2 Eq. LC GHG avoided 4.87 19.43 1.20



### 7.7 .2 Depleted Oil Reservoir
The sensitivity of the CO 2 

### 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 reserv oir 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 CO 2 

### 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 c ost 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 CO 2 

### 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 Rat

e per Well t/d 360 115 5,690
Number of Wells 21 65 2
Levelized Annual CO 2 Storage Cost $/tonne CO 2 Eq. LC GHG avoided 3.82 11.16 1.21



### 7.7.3 Aquifer
The sensitivity of the CO 2 

### storage cost for the aquifer option is determined for the same five key par ameters 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 CO 2 

### injectiv ity 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 CO 2 

### 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 f

or aquiferDepleted 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 Aq uifer
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%



### Th e results for the high and low cost cases as well as the base case are given in Table 7

- 20 . The range of CO 2 

### 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 CO 2 Storage Cost $/tonne CO 2 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 tu

rn cause a dramatic increase in the CO 2 

### 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 t hickness 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 Tab le 7

- 21 . All of the studies are concerned with the injection of CO 2 

### into a saline aquifer, except for the ‘IEA depleted reservoir’ study that looks at storing CO 2 

### in an exhausted gas reservoir.
Table 7
- 21 Inj ection schemes’ characteristics

Study CO 2

Flow Rate (t/d) Downhole

Injection Pressure (MPa) Reservoir

Pressure (MPa) Thickness

(m) Depth

(m) Permeability

(md) Number

of Wells Location

IEA aquifer 8 10,685 28.0 1 1.3 55 1,459 13 6 Onshore IEA depleted reservoir 8 8,560 10.4 3.0

- 2,500 100 4 Onshore

Elsamprojekt 19 3,770

- - - 1,100

- 12 Onshore
GEODISC 20 15,780 17.4 17.2 400 1,600 300 4 Offshore Sleipner West 21 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 CO 2 

### injectivity c annot 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 CO 2 

### injectivity, the value calculated by the model generally agrees with the value used in each of the studies. Indeed, the same relationship between CO 2 

### mobility and injectivity, as is used in the model, has been used in the ‘GEODISC’ study 22 

### and pos sibly 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 CO 2 Injectivity as a Function of CO 2 Mobility

y = 0.0208x

0.10 1.00 10.00 100.00

10 100 1000 10000 CO 2 mobility (md/mPa.s)CO2injectivity (t/d/m/MPa)

IEA aquifer study GEODISC Sleipner West 'Law and Bachu' values

Figure 7
- 8 Comparison of CO 2 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 val ue 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 thre e 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 = 0.0888e 0.0008x

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 drillin g cost valuesDepleted Gas and Oil Reservoir/Aquifer Storage



### 7

- 29

Injecting CO 2 

### 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 g ives 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. Seco ndly, CO 2 

### 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, respectively 20 

### . If CO 2 

### 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 = 1.3555e 0.0004x

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 valuesDepleted Gas and Oil Reservoir/Aquifer Storage



### 7

- 30 

## 7.9 References

1 Holloway, S., J.P. Heederik, L.G.H. van der Meer, I. Czer nichowski

- 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 Environme ntal 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 Technolog y , 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 Kaar stad, 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://ww w.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. McC ann, “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 trappin g

–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 Ass essment 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 As sociation 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 Inter national 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 formatio n,” [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 CO 2 

### into the deep ocean via a pipeline laid on the seabed.



## 8.2 State of the Art
The direct injection of CO 2 

### into the ocean requires starting with a fairly concentrated stream of CO 2 

### 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 CO 2 

### needs to be injected below the thermocline at depths greater than 1000 meters. 1 

### The CO 2 

### may be injected at moderate depths of 1,000 to 2,000 m or at depths greater than 3,000 m. The CO 2 

### can be sequestered either by dissolution in the water column or by the formation of CO 2 

### hydrates, which are solid, ice-like compounds.
Led by offshore exploration and production activi ties 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. Ther efore, as a first step, it appears that the best strategy is to discharge the CO 2 

### below the thermocline at moderate depths of 1,000 to 2,000 m. 2


### The technology to proceed with this option is availa ble. 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 biogeoch emical cycles of the oceans. This storage option is also limited by the fact that it is best suited to large, stationary CO 2 

### sources with access to deep-sea sequestration sites — sources that may account for onl y about 15 to 20 percent of our anthropogenic CO 2 

### emissions.
Figure 8-1 illustrates that about18 percent of worldwide power plant emissions are within a 400 km offshore distance of 1,500 m water depth. Given that power plant CO 2 

### emissions account for about 35 percent of total anthropogenic CO 2 

### emissions, about 6 to 6.5 percent of CO 2 

### emissions could be sequestered in the deep ocean from coastal power plants.Ocean Via Pipeline



### 8

- 2 Cumulative Distribution of Population and CO 2 

### 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 CO 2 emissions



## 8.3 Process Descr iption
To make a more realistic case, it is assumed that three IGCC power plants supply CO 2 

### to a single ocean pipeline. Based on this, the ocean pipeline system needs to be designed to handle three times the quantity of CO 2 

### , i.e. 11.29 million scm (22,167 tonnes), as opposed to 3.76 million scm (7,389 tonnes) of CO 2 

### per day. At the shoreline collection point, additional compression is needed to bring the CO 2 

### up to the ocean pipeline’s required inlet pressure of 152 bar. As a final note, it is assumed tha t 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 CO 2 

### via a subsea pipeline from the shoreline to a depth of 2,000 m, at which depth the CO 2 

### 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 CO 2 

### . These two steps are explained in greater detail below. Figure 8

- 2 gives an ov erview 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 CO 2 

### 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 CO 2 

### overland pipeline transport, the pressure drop per unit l ength is simply found as the difference between the pipeline CO 2 

### inlet and outlet pressures divided by the pipeline length. The pipeline ocean CO 2 

### 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 CO 2 

### be discharged at a pressure equal to the hydrostatic pressure.
The pipeline CO 2 

### 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 CO 2 

### 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 2,0 00 m, is calculated to be approximately 200 bar. A diffuser head loss of 20 bar is then assumed. Next, calculating the average value of CO 2 

### specific gravity over both the 0 to 1,000 m

- depth and 1,000 to 2,000 m

depth intervals, and adding the respective CO 2 

### 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 sensitiv ity 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 CO 2 

### mass flow rate. Inputs: CO 2 

### mass flow rate Pipeline length Injection depth CO 2 

### inlet pressure Diffuser head loss Capital charge rate Outp uts: Total capital cost Total O&M cost Total annual cost Total cost per tonne CO 2 

### OCEAN PIPELINE MODEL Internal Calcs: CO 2 

### 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 & maintenanceOcean Via Pipeline



### 8

- 4 Diameter as a Function of CO 2 

### Mass Flow Rate

0 5 10 15 20 25 30 35 40

0 20 40 60

Mass flow rate (Mt CO 2 

### /yr)Pipe diameter (in)

1 inch = 0.254 meters Figure 8
- 3 Diameter for the base case as a function of CO 2 mass flow rate



### 8.4.2 Cost Calculations

The cost of the subs ea pipeline has been determined based on cost information contained in McDermott’s phase II final report on ‘Large

- scale CO 2 

### 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 Inje ction Depth m 2,000
CO 2 Inlet Pressure MPa 15.2
CO 2 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 metersOcean 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 CO 2 

### overland pipeline transport. This is despite the value of maximum allowab le pressure drop per unit length also being larger. This can be explained by the fact that the design CO 2 

### 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 Co st 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 CO 2 

### 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 d etailed 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% CO 2 Capture Cost ($/tonne captured) $14.55 Land Pipeline Distance 100 km COE Including CO 2 Capture Cost ($/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 metersOcean 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 CO 2 Stored (M tonne/yr) 6.473 Capital O&M Injection Depth (m) 2,000 CO 2 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 CO 2 Storage Costs (years 1-20) CO 2 Captured Basis LC Avoided GHG Basis (storage only) CO 2 Storage Cost, M $/yr 35.75 35.75 CO 2 Stored or Avoided, M tonne/yr 6.47 6.46 Storage Cost, $/tonne CO 2 5.52 5.53

2. Levelized Annual CO 2 Capture and Storage Costs (years 1-20) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

CO 2 Capture Cost, M $/yr 94.17 94.17 94.17 CO 2 Storage Cost, M $/yr 35.75 35.75 35.75 Capture + Storage Cost, M$/yr 129.92 129.92 129.92 CO 2 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. CO 2 Capture and Storage Costs, NPV Basis (years 1-100) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

NPV of CO 2 Capture Costs, M$ 1,072.27 1,072.27 1,072.27 NPV of CO 2 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 CO 2 Stored or Avoided, M tonne 73.70 62.21 61.52 Capture + Storage Cost (NPV Basis), $/tonne CO 2 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 CO 2

Capture & Storage



## 8.7 Sensitivity Analysis
The sensitivity of the CO 2 

### st orage 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 dis tance 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 CO 2 

### 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 t he 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 Co st 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 CO 2 

### 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 CO 2 Storage Cost $/tonne CO 2 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 CO 2 

### by subsea pipeline. In all cases, except the ‘GEODISC’ study, the pipeline is to be used for the purpose of injecting CO 2 

### into the ocean. ‘GEODISC’ looks at subsea pipeline CO 2 

### transport in the context of storing the gas in an offshore aquifer.
Table 8
- 8 Subsea pipelines’ characteris tics

Study CO 2 flow

rate (Mt/y) Initial CO 2

pressure (bar) Diamete

r (m) Lengt

h (km) Injection

depth (m) Recompressi

on station included

IEA Ocean 6 19.00 74 (liquid CO 2 ) 0.800 100 500 Ignored

British Coal 7 3.63 136 0.350 517 2,000 Yes
GEODISC 8 5.67 205 0. 660 200 100 No

McDermott 9 200 (total)

33.3 (each) 130 0.760 (6 pipes) 500 3,000 Yes

UMass 10 8.20 140 0.600 200 1,000 No



### It should be noted that in the ‘McDermott’ study there is a large quantity of CO 2 

### , taken to be the emissions from forty 500 MW e 

### coal

- burning power stations, being injected into the ocean. This large quantity of CO 2 

### , 200 Mt/y, is determined in the study to require the use of oneOcean 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 mo del, taking into account its inability to allow for bundling, one 0.760 m diameter pipe carrying 33.3 Mt/yr of CO 2 

### 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 b asis 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 co mbination 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 th e cost analysis.
The ‘British Coal’ study, like ‘McDermott’, requires a shore

- based pumping station to transport the CO 2 

### a long distance. The cost of this pumping station is not included in the total cost of the scheme, calculated for the purpose of compa ring 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, CO 2 

### .

8.8.2 Comparison of Values from Model and Studies The graph below in Figure 8

- 4 s hows pipe diameter as a function of CO 2 

### 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 CO 2 

### Mass Flow Rate

0 5 10 15 20 25 30 35 40

0 10 20 30 40 50 60 Mass flow rate (Mt CO 2 

### /yr)Pipe diameter (in)

Ocean Pipeline Model (Base Case) IEA ocean study British Coal GEODISC McDermott UMassOcean 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 CO 2 

### mass flow rate. As for pipe diameter, the capital cost v alues 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 CO 2 

### /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 valuesOcean Via Pipeline



### 8

- 13



## 8.9 References

1 Herzog, H., ”Ocean Sequestration of CO 2 

### : 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/repo rts/rd/chap5.pdf

3 Sarv, H., “Large

- scale CO 2 

### 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 P rogramme, Cheltenham. Tech. Rep. IEAGHG/SR3, Jun. 1994.

7 Summerfield, I.R., S.H. Goldthorpe, N. Williams and A. Sheikh, “Costs of CO 2 

### disposal options,” Energy Convers. Mgmt , vol. 34, no. 9

- 11, pp. 1105

- 1112, 1993.

8 Allinson, G. and V. Nguyen, “The e conomics of CO 2 

### sequestration in Australia,” presented at Fifth International Conference on Greenhouse Gas Control Technologies Cairns, Australia, 2000.

9 Sarv, H., “Large

- scale CO 2 

### 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 CO 2 

### and their environmental effects,” Energy Convers. Mgmt , vol. 38, suppl., p p. 279

- 286, 1997.

### 9

- 1 

# 9 OCEAN VIA TANKER

9.1 Introduction
The ocean storage via tanker option involves transporting the CO 2 

### by refrigerated tanker from a port facility to an offshore floating platform, where the CO 2 

### is injected into the deep ocean through a vertical pipe.



## 9.2 St ate 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 CO 2 

### to an offshore floating platform is however within the current state of technology. All that is required i s that existing floating platforms and so

- called ‘semi

refrigerated’ 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 CO 2 

### 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 CO 2 

### to the shoreline collection point. Based on this, the ocean tanker system needs to be designed to handle three times the quantity of CO 2 

### , i.e. , 11.29 million scm (22,167 tonnes), as opposed to 3.76 million scm (7,389 tonnes) of CO 2 

### 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, respect ively.


## 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 CO 2 

### 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 CO 2 

### to the offshore platform is determined. Second, the diameter of the vertical pipe to carry the CO 2 

### from the platform to the injectionOcean Via Tanker



### 9

- 2

depth is calculated. Third, the amount of CO 2 

### 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 CO 2 

### . 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 m 3 

### of CO 2 

### 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 CO 2 

### give a CO 2 

### density of 1155 kg per m 3 

### and a tanker capacity, in terms of weight, of 25,410 tonnes of CO 2 

### . Given this latter tanker CO 2 

### capacity, it is found from the design CO 2 

### 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 loa ding time is not dependent on the CO 2 

### 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 CO 2 

### 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 a dditional 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 CO 2 

### 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 aver age value of CO 2 

### specific gravity over both the 0 to 1,000

- m

- depth and 1,000

- to 2,000

- m

- depth intervals, and adding the respective CO 2 

### 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 : CO 2 density Tanker CO 2 capacity 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 CO 2 PIPELINE DIAMETER CALCULATION Internal Calcs : CO 2 density CO 2 viscosity CO 2 outlet pressure Gravity head Pressure drop per unit length Vertical pipeline diameter TANKER CO 2 EMISSIONS CALCULATION Internal Calcs : Number of round trips Total annual distance traveled Total annual fuel usage Total annual CO 2 emitted Inputs: CO 2 mass flow rate Tanker volume Tanker pressure Tanker temperature Tanker speed Offshore distance Inputs: CO 2 mass flow rate CO 2 inlet pressure CO 2 temperature Injection depth Inputs: Tanker fuel usage Diesel energy content Diesel CO

2 emissions factor Offshore distance

BOIL OFF CO 2 EMISSIONS CALCULATION Internal Calcs : Total annual CO 2 emitted Inputs: CO 2 mass flow rate Boil off

Time taken for round trip

Number of tankers required Pipe diameter

Annual CO 2 emitted bbooff Annual fuel usage Annual CO

2 emitted

Figure 9
- 1 Ocean tanker cost model overview diagramOcean Via Tanker



### 9

- 4 9.4.3 CO

2 

### Emissions Calculations The quantity of CO 2 

### to be emi tted by the tankers needs to be determined. This requires that the quantity of fuel used by a 22,000 m 3



### - 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 Mc Dermott 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, o f 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 gallon 4 

### , the total annual quantity of fuel used by the tanke

rs 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 usag e of 3.91 gallons per km.
Given this calculated value of tanker fuel usage, it is possible to determine the CO 2 

### emitted by the tankers. Diesel fuel has an energy content of around 137 million joule per gallon and a CO 2 

### emissions factor of close to 70 mill igrams per joule. Given that the fuel used by a tanker is 3.91 gallons per km, the amount of CO 2 

### 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
roun d 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 CO 2 

### emitted per km b y the total annual distance traveled gives a total annual amount of CO 2 

### emitted of 2,395 tonnes.

The amount of CO 2 

### emitted due to boil off is estimated as one percent per day of the amount of CO 2 

### transported by tanker based on industry experience with CO 2 

### truck tankers. It is important to note that the calculation assumes that the CO 2 

### is in the tanker, and therefore undergoing boil off, for only one half the number of hours taken for a round trip. This CO 2 

### quantity is then multiplied by 365 days to give t he amount of CO 2 

### emitted per year. The annual quantity of CO 2 

### emitted is calculated to be 53,362 tonnes.

9.4.4 Cost Calculations
The total capital cost of the tanker ocean CO 2 

### storage option comprises the capital cost of the three required tankers, the of fshore 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 floatin g 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 capi tal 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 capitalOcean 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 Sarv 3 , 4 

### , it was learned that the total annual O&M cost in the cas e 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 p ercent 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

Para meter 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 $ 3,0 00,000 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 metersOcean 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 t o calculate the required number of tankers, the diameter of the vertical pipeline, and the CO 2 

### emissions from the tankers and due to boil off. Table 9

- 2 shows the results.
Table 9
- 2 Design basis for ocean sto rage 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 CO 2 Capacity kg/m 3 25,410 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 CO 2 Emissions Factor mg/joule 70 Total Annual Distance Traveled km/y 63,683

Total Annual Fuel Usage gal/y 249,001 CO 2 Emitted by Tankers t/y 2,395 Boil Off %/d 1 CO 2 Emitted by Boil Off t/y 53,362 1 inch = 0.0254 meters, 1 gal = 3.79 litersOcean 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 methodol

ogy 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 Str ucture $ 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 Mode l Results
This section presents costs for CO 2 

### 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% CO 2 Capture Cost ($/tonne captured) $14.55 Pipeline Distance 100 km COE Including CO 2 Capture Cost ($/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 CO 2 boil off rate (%/day) 1.0% Nominal vertical pipe size (in) 8 Number of tankers needed 3 Ocean Tanker Storage Input VariablesOcean 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 ba se case

Process Description Capital & O&M Cost

Pileline Distance 100 km CO 2 Stored (M tonne/yr) 6.473 Capital O&M Injection Depth (m) 2,000 CO 2 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 CO 2 Storage Costs (years 1-20) CO 2 Captured Basis LC Avoided GHG Basis (storage only) CO 2 Storage Cost, M $/yr 113.03 113.03 CO 2 Stored or Avoided, M tonne/yr 6.47 6.41 Storage Cost, $/tonne CO 2 17.46 17.64

2. Levelized Annual CO 2 Capture and Storage Costs (years 1-20) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

CO 2 Capture Cost, M $/yr 94.17 94.17 94.17 CO 2 Storage Cost, M $/yr 113.03 113.03 113.03 Capture + Storage Cost, M$/yr 207.20 207.20 207.20 CO 2 Stored or Avoided, M tonne/yr 6.47 5.41 5.36 Capture + Storage Cost, $/tonne CO 2 32.01 38.29 38.67

3. CO 2 Capture and Storage Costs, NPV Basis (years 1-100) CO 2 Captured Basis CO 2 Avoided Basis LC Avoided GHG Basis

NPV of CO 2 Capture Costs, M$ 1,072.27 1,072.27 1,072.27 NPV of CO 2 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 CO 2 Stored or Avoided, M tonne 73.70 61.61 60.93 Capture + Storage Cost (NPV Basis), $/tonne CO 2 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 CO 2

Capture & StorageOcean Via Tanker



### 9

- 10 

## 9.7 Sensitivity Analysis
The sensitivity of the CO 2 

### 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 pipel

ine 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 CO 2 

### 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 CO 2 

### 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
CO 2 Emitted by Tankers t/yr 2,395 7,186 1,198
CO 2 Emitted by Boil Off t/yr 53,362 139,415 24,638
Levelized Annual CO 2 Storage Cost $/tonne CO 2 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 CO 2 

### transportation and deep ocean sequestration,” McDermott Technology Inc., Ohio. Tech. Rep. DE

- AC26

- 98FT40412, Mar. 1999.

2 Sa rv, H., “Large

- scale CO 2 

### 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. 2 0, 2002.

### 10

- 1 

# 10 CO 2 

# 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 CO 2 

### sink en hancement, (2) changes in GHG emissions other than from CO 2 

### 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 CO 2 

### sink enhancement options. Most of this information relates to developing countries, because that is where the greatest amount of forestry CO 2 

### sink enhancement is l ikely 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, la nd, 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 c osts. 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, re forestation, and avoidance of deforestation, developed and developing countries, and temperate and tropical climates.CO 2 Sink Enhancement in Forests

— Basis and Approach



### 10

- 2 10.2.2 Carbon Sink Enhancement When a forest grows, it removes CO 2 

### from the atmosphere and sequesters carbon in trees and other above gro und 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 CO 2 

### . 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, li tter 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

su stainably 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 CO 2 

### but some will remain sequestered as carbon for many decades. Carbon s tored 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 whic h generate significant amounts of CO 2 

### 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, disp lacing fossil fuels. Production and use of biomass fuels is beyond the scope of this study.

10.2. 4 Non
- CO

2 

### Greenhouse Gases

Forests and other land

- uses emit significant quantities of non

- CO 2 

### greenhouse gases, particularly N 2 

### O. Emissions of N 2 

### O and CH 4 

### ar e taken into account in this study but emissions of other non

greenhouse gases and particulates which can indirectly affect the climate are not. Further information on non

- CO 2 

### greenhouse gases is given in Section 10.3.

10.2. 5 Ancillary Benefits
Forestry p rojects 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.CO 2 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 a lso 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 mor e 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 ancillar y 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, base lines, 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 m

eans. 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 p olitical 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 carb on 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,CO 2 Sink Enhancement in Forests

— Basis and Approach



### 10

- 4 infrastructure may be inadequate, legal or technological barriers may exist etc. Overcoming these barri

ers 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 thi s sense should not be confused with physical leakage of CO 2 

### 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 protectio n 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 f

rom plantations that are subsidi z ed 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 a mount of non

subsidi z ed plantations may decrease, resulting in less carbon sequestration. A study of the impacts on timber markets of 50 million ha of sequestration forests 1 

### predicted that the impacts on the net quantity of carbon sequestered would be fai rly 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 intensifica tion 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 CO 2 

### emitted during production of fertili z ers and increased emissions of N 2 

### O 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 CO 2 

### 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 te mporary emission of CO 2 

### but in the long term the carbonCO 2 Sink Enhancement in Forests

— Basis and Approach



### 10

- 5 sequestration at the site could be restored, although there would be an economic cost of re

establishing 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 change 2 

### . Temperate grasslands are also expected to expand into regions of Europe and North America that are currently dominated by temperate or coniferous forests. A

reas 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 region 3 

### . 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 predicti ons 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 models 4 

### has indicated that in some parts of the world, including large parts of Ca nada 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 sequestra tion 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

ar e described in detail in Chapters 11

- 17.CO 2 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 fi r 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, description s 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 propos ed.
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 s equestered 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 onl y provided for typical sites.CO 2 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 summari z ed 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 

### YearCarbon 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 q uantity 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 woo d 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 cos ts 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 o f 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 th e differences are less than between the different forest cases.CO 2 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 YearsCarbon 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 e asy 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 include s 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. Som e 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 uncerta inties 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 sho wn 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.CO 2 Sink Enhancement in Forests

— Basis and Approach



### 10

- 9 0.0 50.0 100.0 150.0 200.0 250.0 300.0Carbon 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 producti on, 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/m 3 

### of final wood product has been suggested 5 

### . 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
- CO

2 

### Greenhouse Gases
Greenhouse gases other than CO 2 

### 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

- CO 2 

### GHG emissions from forests . Non

- CO 2 

### greenhouse gases are not normally included in assessments of forestry sequestration schemes. IncludingCO 2 Sink Enhancement in Forests

— Basis and Approach



### 10

- 10 non

- CO 2 

### GHGs would greatly increase data requirements and monitoring costs. In this study,
non

- CO 2 

### greenhouse gases are included as a sensit ivity 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 N 2 

### O sources related to land

- use are summari z ed 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 N 2 

### O from natural forests are 1.1 Gt carbon

- equivalent per year, equivalent to about 18 percent of current emissions of CO 2 

### from fossil fuel u se. Emissions of N 2 

### O 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 N 2 

### O emissions from forests is large, particularly for temperate fo rests, where the range of uncertainty is a factor of 16.
Table 10
- 2 Global N 2 O 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
Livestoc k 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 uncertain 6 

### . 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 N 2 

### O.CO 2 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 N 2 

### O and methane emission s of forestry sequestration depends on the emissions from the baseline land use. Forests are large emitters of N 2 

### O 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, i f 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 N 2 O And CH 4 emissions

U.S. afforestation / deforestation Non

- U.S. afforestation / deforestation Avoidance of deforestation
CH 4 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
N 2 O 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 - 1 0.06 0.13

- 0.13
Note: emissions for avoidance of deforestation are per hecta re 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 time 9 

### . Most of the rest is oxidized and released to the atmosphere as CO 2 

### . Some methane and N 2 

### O are also produced during open burning of bioma ss 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 data 9 

### . The carbon equivalent of methane and nitrous oxide missions from open burning of biomass appea rs to be approximately equal to the quantity of carbon that is converted to charcoal and therefore not emitted as CO 2 

### . For this study it is therefore assumed that, in effect, all of the carbon released during deforestation and ground clearance prior to pl

anting of forests is released as CO 2 

### .CO 2 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 durin g biomass burning, along with CO and NO x 

### (NO and NO 2 

### ). 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 t he 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 develo ped 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 carbo n 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 developi ng 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 subsist ence 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 be ing 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.CO 2 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 s ome 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 unce rtain 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 fore st 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 precision 6 

### . Th ird party organi z

ations 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 percent 6 

### . If projects could only claim carbon credits for the lower bound of the confidence in terval 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 av ailable information 6 

### , 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 CO 2 

### capture and storage options included in this project. The ratio nale 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 investmen ts regardless of whether the investments involve CO 2 

### capture and storage or CO 2 

### 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 pe rcent (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.CO 2 Sink Enhancement in Forests

— Basis and Approach



### 10

- 14 Taxable income was based on revenues from a given harvest minus expe nses 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 othe r CO 2 

### 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 Ch ange), 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., Ni trous 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 Inven tories 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 m 3 

### 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 natur ally 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 m oderately 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 L

oblolly 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 produc tivity 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 u nder 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

• applicatio n of P and N fertili z ers 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 releas e 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 optio n 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 in dicate 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 220m 3 45 years

Medium 20

- 23 Sand hills and plains (deep sands) 190m 3 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 ar e 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 treatmen ts 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 st ates. 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 o r 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 wo uld 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 arou nd 200m

3 

### timber at the end of the rotation. At $22/m 3 

### , 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 p resented in Table 11

- 3. The cost of CO 2 

### 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 m 3 

### /ha and other input variables are the same as for the medium productivity case. Costs of CO 2 

### 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.

Cos ts 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 CO 2 

### 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 ($/ha) 50 Maintenance (years) 4
Timber revenue ($/m 3 

### ) 22 Time Between Timber yield (m 3 

### /ha) : Harvests (yrs) End of rotation 190 45 Thinning 1 Thinning 2 

## Forestry: Input VariablesCase 1: Southern U.S. Loblolly Pine Plantations



### 11

- 6 Table 11
- 4 Forestry summary of results for Loblolly Pine medium productivity case

CO 2 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-CO 2 GHGs 66.28 Above ground carbon with timber revenues 115.94 Above & below ground carbon, non-CO 2 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

YearsCarbon 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
YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 1: Southern U.S. Loblolly Pine Plantations



### 11

- 7

Table 11
- 5 Forestry summary of results for Loblolly Pine high productivity case

CO 2 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-CO 2 GHGs 49.29 Above ground carbon with timber revenues 74.27 Above & below ground carbon, non-CO 2 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

YearsCarbon 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

YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 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 F orests, 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 o f 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 NORT HWEST 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 rotatio ns 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 c an 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 seedl

ings. Light shade improves survival of first

- year seedlings but thereafter control of competing vegetation isCase 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 provid e 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 p recipitation: >1500 mm

Soils: well

- aerated, deep soils >1000m 3
100 years

Medium 18

- 24 Intermediate >700m 3
100 years

Low n/a Annual precipitation: <1000 mm Soils: poorly drained, nutrient poor soils Not availableCase 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 assumpti ons 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

- gr ound 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 c osts 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 m 3 

### /ha, giving an income of $33,000

- $80,000 at current prices, with a fina l 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 CO 2 

### sink enhancement ($/tonne C) is presented on se veral 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 m 3 

### /ha for thin nings 1 and 2, respectively, and other input variables are the same as for the medium productivity case. Costs of CO 2 

### 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 describe d in Chapter 2, and can be compared with costs for CO 2 

### 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 c ases 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 variabl es 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 ($/ha) 20 Maintenance (years) 4
Timber revenue ($/m 3 

### ) 150 Time Between Timber yield (m 3 

### /ha) : Harvests (yrs) End of rotation Thinning 1 135 50 Thinning 2 215 70 

## Forestry: Input VariablesCase 2: Pacific Northwest Douglas Fir Plantations



### 12

- 6 Table 12
- 4 Forestry summary of results for Douglas Fir medium productivity case

CO 2 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-CO 2 GHGs 23.61 Above ground carbon with timber revenues 14.01 Above & below ground carbon, non-CO 2 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 YearsCarbon 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

YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 2: Pacific Northwest Douglas Fir Plantations



### 12

- 7

Table 12
- 5 Forestry summary of results for Douglas Fir high productivity case

CO 2 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-CO 2 GHGs 18.92 Above ground carbon with timber revenues 4.99 Above & below ground carbon, non-CO 2 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

YearsCarbon 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

YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 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 Fo rest 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 Tech nical Report GPL

- GTR

- 116. US Department of Agriculture, Forest Service, Washington, DC.

### 13

- 1 

# 13 CASE 3: SPANISH CEDA R PLANTATIONS IN MEX ICO USING THE TAUNGYA SY STEM

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 th e 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 pla ntation, 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 a t 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. Cedr ela 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. T hinning 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 h oles 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 bu t 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 contin ue 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 dur ing 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 reali z e the full value of this species.Case 3: Spanish Cedar Plantations in Mex ico 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 ha rvested 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: 145 0

–

3000 mm/y Soil: Well

- drained, brown

- black 450 m 3
25y

Medium Altitude: <300 or >1200 m

Rainfall: <1450 mm/y Soil: Heavy clays; sandy 400 m 3
25 y

Low Altitude: <300 or >1200 m

Rainfall: <1450 mm/y Soil: Stony, compacted or oxidised clays 350 m 3
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 interimCase 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
es tablishment 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
Shr ubs 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 cla sses 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 carb on (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 147Case 3: Spanish Cedar Plantations in Mex ico 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 e stimated 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 vi rtually 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 m 3 

### /ha at a price of $100 /m 3 

### 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 CO 2 

### 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 m 3 

### /ha, time between harvest is 30 years, and other input variable s are the same as for the medium productivity case. Costs of CO 2 

### 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) ar e 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 m 3 

### /ha, time between harvest is 25 years, and other input variables are the same as for the medium productivity case. Costs of CO 2 

### 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 pro ductivity 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 CO 2 

### capture and storage and other sink enhancement options calculated on the same NPV basis. The same establishment, land, maintenance, and harvestingCase 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 cos ts 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 abo veground 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 ($/ha) 25 Maintenance (years) 7
Timber revenue ($/m 3 

### ) 100 Time Between Timber yield (m 3 

### /ha) : Harvests (yrs) End of rotation 400 25 Thinning 1 Thinning 2 

## Forestry: Input VariablesCase 3: Spanish Cedar Plantations in Mex ico Using the Taungya System



### 13

- 7

Table 13
- 5 Forestry summary of results for Spanish Cedar medium productivity case

CO 2 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-CO 2 GHGs 14.67 Above ground carbon with timber revenues (101.36) Above & below ground carbon, non-CO 2 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

YearsCarbon 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

YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 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

CO 2 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-CO 2 GHGs 18.46 Above ground carbon with timber revenues (68.69) Above & below ground carbon, non-CO 2 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

YearsCarbon 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

YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 3: Spanish Cedar Plantations in Mex ico Using the Taungya System



### 13

- 9

Table 13
- 7 Forestry summary of results for Spanish Cedar high prod uctivity case

CO 2 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-CO 2 GHGs 11.41 Above ground carbon with timber revenues (90.70) Above & below ground carbon, non-CO 2 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
YearsCarbon 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

YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 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 not e 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 for estry 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 secondar y 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 t imber. 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 Ecology 1,2 ,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 importan t 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, o rchids 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, timbe r 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 wid e 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 soils 1 

### 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 Mainte nance 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 o f 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 t he plot suitable for replanting with pine seedlings combined with cultivation of maize for the first 3

- 4 years.Case 4: Restoration of Degrad ed 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 conditions 1 

### ; site productivity is c lassified by soil characteristics (under development) and the production of maize 5



### . 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 m 3
30 y

Medium Altitude: 700

- 2000 m

Rainfall: 1200

- 1500 mm

Soil: Intermediate 280 m 3
35 y

Low Altitude: <700 or >2000 m

Rainfall: <1200 mm/y Soil: Impermeable clays or stony <20cm depth 220 m 3
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 si te. 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 veg etation 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 substa ntiated 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 wi ll accumulate carbon at 5 to 12 tC ha - 1 

### y

- 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 s

torage 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 121Case 4: Restoration of Degrad ed 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 t his 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 P otential Income

300 m 3 

### of pine timber/ha could be produced, at a price of $30/m 3 

### , 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 y ield 2



### . 

## 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 CO 2 

### 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 m 3 

### /ha, time between harvest is 35 y ears, and other input variables are the same as for the medium productivity case. Costs of CO 2 

### sink enhancement ($/tonne C) are presented for the low productivity case on several accounting bases in Table 14

- 6. Cumulative changes in carbon sequestered (t onne 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 m 3 

### /ha, time between harvest is 30 years, and other input variables are the same a s for the medium productivity case. Costs of CO 2 

### 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 bel

ow 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 CO 2 

### capture and storage and other sink enhancement options calculated on the same NPV b asis. 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 aCase 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 ($/ha) 60 Maintenance (years) 7
Timber revenue ($/m 3 

### ) 30 Time Between Timber yield (m 3 

### /ha) : Harvests (yrs) End of rotation 280 35 Thinning 1 Thinning 2 

## Forestry: Input VariablesCase 4: Restoration of Degrad ed 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

CO 2 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-CO 2 GHGs 13.03 Above ground carbon with timber revenues 3.54 Above & below ground carbon, non-CO 2 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

YearsCarbon 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

YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 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

CO 2 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-CO 2 GHGs 19.07 Above ground carbon with timber revenues 9.68 Above & below ground carbon, non-CO 2 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 YearsCarbon 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

YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 4: Restoration of Degrad ed 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

CO 2 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-CO 2 GHGs 11.11 Above ground carbon with timber revenues (5.00) Above & below ground carbon, non-CO 2 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

YearsCarbon 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

YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 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 t his 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 1 5, 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 technic al 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 tz eltal y tojolabal del estado de Chiapas. Cuadernos de Trabajo 4, Instituto Nacional de Ecología, Mexico

### 15

- 1 

# 15 CASE 5: RESTORATION OF DEGRADED DRY MIOM BO 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, fuelw ood 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 p rogramme 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 Isoberl inia . Miombo occurs across approximately 2.8 million km 2 

### 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. Dr y 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 Mio mbo Woodland in Southern Africa



### 15

- 2

15.3.1 Establishment Establishment involves planting of nursery

- grown seed lings 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 nigr enscens, 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 30m 3 

### /ha over a 30

- ye ar 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 woodl ands 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.

• Veget ation: 6 tC/haCase 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 Miomb o 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 (aro und 1 m 3 

### per hectare per year) some of the species produce very valuable timber (fetching between $500 and $1000/m 3 

### on international markets). Other major benefits of restoration of miombo woodland are reduction in soil erosion and further environmental d egradation, 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 fuelw ood 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 (typical

ly kerosene) for domestic energy. The CO 2 

### 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 CO 2 

### 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 productivi ty 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 CO 2 

### capture and storage and other sink enhancement options cal culated 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 ($/ha) 40 Maintenance (years) 4
Timber revenue ($/m 3 

### ) 500 Annually after Timber yield (m 3 

### /ha) : Year End of rotation Thinning 1 1 15 Thinning 2 

## Forestry: Input VariablesCase 5: Restoration of Degraded Dry Miombo Woodland in Southern Africa



### 15

- 6 Table 15
- 3 Forestry summary of results for Miombo medium productivity case

CO 2 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-CO 2 GHGs 14.62 Above ground carbon with timber revenues (139.74) Above & below ground carbon, non-CO 2 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 YearsCarbon 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

YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 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 . C entre 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 Land

Cover Change in th

e 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 . Une sco, Switzerland

.

### 16

- 1 

# 16 CASE 6: AGRO

- FORESTRY: MANGO AND NATIVE FRUIT ORCHARDS IN SE MI
- 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 g reater 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 tre es 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
s upplement 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 i s 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 tr ees 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 year s. 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 strong ly 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 sub

soil ; te mp 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 h a - 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 fruit

setting (February

–

April). 5 t ha - 1 (5 y)
10 t ha - 1 (10 y)
15 t ha - 1 (20 y)



## 16.4 Management Operatio ns
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 befor e. 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

- cu ltivation 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 plo t, legumes, millet and groundnuts are planted in rows up to the 12 th 

### 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. TheCase 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. Ponge mia, 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 St orage 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 Scie nce 4



### . 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 carb on (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 establi shment, 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 r eliable 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 m

uch 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 or der 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 theCase 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 ass umed 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

Whi le 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 opti on. 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 o f promising examples of bio

fuelled generators and water pumps have been established.


## 16.8 Summary of Model Inputs and Results

A summary of input variables for the base case (medium productivity) Mango

- tamarind is presented in Table 16

- 3. The cost of CO 2 

### 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 an

d 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 CO 2 

### 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 T able 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 ($/ha) 125 Maintenance (years) 5
Timber revenue ($/m 3 

### ) 400 $/y fruit
Timber yield (m 3 

### /ha) : End of rotation Thinning 1 Thinning 2 

## Forestry: Input VariablesCase 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

CO 2 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-CO 2 GHGs 154.10 Above ground carbon with timber revenues (181.20) Above & below ground carbon, non-CO 2 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 YearsCarbon 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

YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 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 K olar 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 S cience, 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 th e 1990s range from 1.1 to 1.7 GtC/y, with a best estimate of 1.6 GtC/y 1 

### . In tropical countries with large areas of remaining forest, reduction of deforestation has much greater potential climate benefits than other forestry options. Reduction of deforest ation 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 discr ete 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 tropic al 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 o f canopy species. They occur mainly at altitudes of <500 m and rainfall of
> 2000 mm/y.Case 7: Avoidanc e 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.

Th e 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 coloni zation , 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 l oss 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 respons e 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 lo ss of terrestrial carbon to key driving factors that exist within a particular region 2 

### . 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 f or % 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 so uthern 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 den sities 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 predictio n 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 ite rations, 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 perc entage 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. Tota l 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.

Distance to road (m) Population density (person/km 2 ) 500 500
- 1500 1500
>40 63 55 41
20

- 40 56 46 37
0

- 20 46 35 25
0 41 28 8Case 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 matri x 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 202 0, 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 sho wn in Table 17

- 2Case 7: Avoidance of Deforestation in Chiapas, Mexico



### 17

- 7

Table 17
- 2 Predicted annual rate of deforestation (%).

Vulnerability Base rate Population

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
Medi um 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 mo ist 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
av oid 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 maintai ned. 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 Ch iapas 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 substa ntial opportunity costs due to the value of charcoal and bromeliads that are currently extracted at significantly higher rates than theCase 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 assoc iated 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 i

nput 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 CO 2 

### sink enhancement ($/tonne C) is presented on several account ing 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 CO 2 

### 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 ($/ha) 25 Maintenance (years) 4
Timber revenue ($/m 3 )
Timber yield (m 3 /ha) : End of rotation Thinning 1 Thinning 2 

## Forestry: Input VariablesCase 7: Avoidance of Deforestation in Chiapas, Mexico



### 17

- 10 Table 17
- 4 Forestry summary of results for Avoidance of Deforestation medium productivity case

CO 2 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-CO 2 GHGs 9.87 Above ground carbon with timber revenues 9.68 Above & below ground carbon, non-CO 2 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 YearsCarbon 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 YearsCost or Net Cost($/ha) Undiscounted Net Cost Discounted Net Cost Undiscounted Cost Discounted CostCase 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 CO 2 

# SINK ENHANCEMENT VI A 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 po tential (75

- 208 million tonnes of carbon/year) for CO 2 

### sink enhancement in U.S. croplands 1 

### . The CO 2 

### sink enhancement in croplands is the result of increased carbon storage in soil organic matter. The remaining one

- half of the potential for CO 2 

### sink enhan cement 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 carbo n equivalent GHG avoided) from reducing tillage on U.S. croplands. This involves estimating the following aspects of reducing tillage over time: (1) levels of CO 2 

### sink enhancement, (2) changes in GHG emissions other than from CO 2 

### 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) monitori ng 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

Researc h, 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 CO 2 

### sink enhancement, there a re many benefits of reducing tillage such as fuel, labor, and equipment savings, improved soil quality, and reduced soil erosion by both water and wind 2 

### . 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 Center 3 

### are used in this report. These definitions are as follows:Economics of CO 2 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 dist urbance 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 m ay 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 t he 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 accomp lished 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 p rior 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)

— F ull

- 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 equiva lent 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 an

d/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 t rips. 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 sy stem. These estimates have been published for Iowa 4 

### , Indiana 5 

### , and Nebraska 6 

### 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 intensiv

e

- tillage.Economics of CO 2 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 CO 2 

### from the atmosphere and incorporates the CO 2 

### into crop residues via photosynthesis (Figure 18

- 1). As the crop residues decompose, they release CO 2 

### 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 m atter 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 orga nic 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 CO 2 

### from eroded soil.



## Conservation

- Tillage System & Residue Management

Harvested Biomass Crop

Residue Carbon Production Soil Organic

Matter

Eroded Soil CO 2 

## CO 2 

## CO 2

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 reasons 7 

### . One key rea son 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 de composition.
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 ofEconomics of CO 2 Sink Enhancement Via Reducing Tillage on U.S. Crop land

— Basis and Approach



### 18

- 4 reducing tillage. An example of reaching a new steady

- state level of soil organic carbon ( ? SOCs) is illustrated in Figur e 18

- 2. The data points for 0, 10, and 20 years are model predictions from the Iowa Carbon Storage Project 4 

### 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 org anic 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

u ntil 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 vege tation.Economics of CO 2 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 precipitatio n levels and the associated increase in crop production and amount of crop residues added to the soil 7



### . 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 CO 2 Sink Enhancement Via Reducing Tillage on U.S. Crop land

— 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 Mou ntains 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/f allow 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 yea r 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 rel ationships are presented for an eastern Colorado location in Figure 18

- 5 which is based on model simulations 10



### . 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/fallow 11



### .Economics of CO 2 Sink Enhancement Via Reducing Tillage on U.S. Cropland

— Basis and Approach



### 18

- 7 Figure 18
- 5 Simul ated 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, conventionally

tilled 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 cou ld 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, India na, 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 t hese 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 CO 2 Sink Enhancement Via Reducing Tillage on U.S. Crop land

— 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 evid ent 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 inco me 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 per cent 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 percentEconomics of CO 2 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
sec ond 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 c an 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 we re discussed above for Table 18

- 1.Economics of CO 2 Sink Enhancement Via Reducing Tillage on U.S. Crop land

— 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 hectar es % 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 Sm

all 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 t han 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 wheat

fallow systems.Economics of CO 2 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 sma ll 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 CO 2 Sink Enhancement Via Reducing Tillage on U.S. Crop land

— Basis and Approach



### 18

- 12 Table 18
- 4 Adoption of conservation
- p in major cotton state s 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 Cot ton 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 (N 2 

### O) 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 emi ssions. 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 CO 2 

### emitted from fuel use.Economics of CO 2 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 reduce s 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 ammon ia and downstream N fertilizers such as urea and urea

ammonium 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 CO 2 

### 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 increa se 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 N

2 

### O Emissions From Soil

Increases in N 2 

### O emissions can have a large impact on the overall amount of GHG avoided by reducing tillage. Small differences in N 2 

### O emissions can be important because N 2 

### O is 296 times more potent (weight basis) than CO 2 

### as a GHG. Reducing tillage increases the potential for increased N 2 

### O emissions from the soil, but this poten tial is not necessarily realized if these systems are well managed. Emission of N 2 

### O from soils results from both denitrification and nitrification (more details about these processes in the next paragraph). In general, the amount of N 2 

### O 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 N 2 

### O may be emitted when tillage is reduce d. This is because, in some cases, soil conditions with reduced tillage are more conducive to N 2 

### O emissions from denitrification and nitrification. Therefore, both the amount of N fertilizer applied and soil factors must be considered in estimating the e ffects of reducing tillage on N 2 

### O emissions.Economics of CO 2 Sink Enhancement Via Reducing Tillage on U.S. Crop land

— Basis and Approach



### 18

- 14 Denitrification is a microbial process in which nitrate nitrogen is converted to gases, mainly N 2 

### and N 2 

### O, under limited oxygen conditions 13 

### . Nitrous oxide is usually a minor portion of the N lost via denitrifi cation. 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 s oil 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 N 2 

### O emissions with conservation

till 14 

### . More specifics will be provided in the case studies.

Nitrification is the microbial conversion of ammonium to ni trate. 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 ni trate via nitrification. Nitrification is a less important source of N 2 

### O than is denitrification and effects of reducing tillage on N 2 

### O emissions from nitrification are less well understood than the effects of reducing tillage on N 2 

### O emissions from denitr ification

13



### .



## 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 pa y 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 gener al, crop production costs are less for conservation

- tillage systems than for intensive

tillage 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 i ncomes 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 year 15 

### . 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

- tilla ge systems is usually greatest during the first few years after switching to conservation tillage 16



### . This is partly because farmers have to learn how to adapt conservation

- tillage systems to their specific soils, climate, and financial resources and partl y 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 yie lds. 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 overEconomics of CO 2 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 CO 2 

### sink enhancement via reducing tillage are transaction costs for aggregating and bro kering CO 2 

### 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 enhancem ent 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, afte r 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 adopt ion 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 se vere 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 le vel 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 CO 2 

### abatement from an energy company perspective if farmers were to permane ntly 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 CO 2 

### abatement and CO 2 

### abatement costs. This approach was selected to enable comparison of CO 2 

### storage and sink enhancement options that vary greatly in the timing and permanence of CO 2 

### abatement and timing of CO 2 

### abatement costs. We treat removals and emissions/leaks of CO 2 

### as separate events. The basic idea is that when a company removes or pays for removal of a ton of CO 2 

### , the company receives the going price of CO 2 

### . When a ton of CO 2 

### is released, the owner of this CO 2 

### (in this case the company) must then purchase a credi t from elsewhere at the going price. With this approach, a breakeven CO 2 

### price ($/tonne of CO 2 

### ) is calculated as the price at which the sum of discounted CO 2 

### revenues (both positive and negative) equals the sum of discounted costs of CO 2 

### sink enhancement. This breakeven price is the long

- term cost of CO 2 

### abatement. See Section 2.4 for calculating costs of storing captured CO 2 

### for more details on calculating the breakeven CO 2 

### price.Economics of CO 2 Sink Enhancement Via Reducing Tillage on U.S. Crop land

— Basis and Approach



### 18

- 16 A 6.09 percent after

- tax discount rate was used in calculating breakeven CO 2 

### prices for CO 2 

### 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 CO 2 

### , and is applicable for calculating CO 2 

### 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 CO 2 

### capture and storage or CO 2 

### sink enhancement.


## 18.8 Cropland Case Studies Includ ed 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 a nd worst

- case scenarios concerning effects of reducing tillage on N fertilizer requirements and on N 2 

### O 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 ca se includes best

- case and worst

- case scenarios concerning effects of reducing tillage on N fertilizer requirements and on N 2 

### O emissions.

3

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

- till to no

- till. This ca se includes variations in the amount of SOC sequestered and amount of N 2 

### O 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 am ount of N 2 

### O emissions due to reducing tillage.Economics of CO 2 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 Conservati

on 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 Eco logy 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 gree nhouse 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. C ollins, 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.jpgEconomics of CO 2 Sink Enhancement Via Reducing Tillage on U.S. Crop land

— Basis and Approach



### 18

- 18 10 Del G rosso, 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. Colo rado 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, an

d 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., Cath erine 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

1 6 CTICc (Conservation Tillage Information Center c). 2002. http://www.ctic.purdue.edu/Core4/MAX/1998/Page38.html

### 19

- 1 

# 19 CORN/SOYBEAN ROTATIO NS 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 res ults 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/s oybean rotations in the Corn Belt states, additional adoption of conservation

tillage will be required on a state scale to avoid the same quantity of GHG emissions as avoided in the CO 2 

### capture and storage cases assessed in this project. For example, conv erting the base

case 425 MW (net) integrated gasification combined cycle (IGCC) power plant to capture 90 percent of the CO 2 

### produced and storing the captured CO 2 

### 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 emissi ons 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 am ount 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: N o - till Moderate

- till No

till Moderate

- till

Quadratic SOC response curve Δ SOCs, 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 N 2 O (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 N 2 O 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 Ye ars 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 44Corn/Soybean Rotations in the U.S. Corn Belt



### 19

- 3

19.2.1 Increases in Soil Organic Ca rbon
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 Iowa 1 

### and Indiana 2 

### . Iowa and Indiana form a representative east

- west transect across the mid

- secti on 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 intensiv e

- 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 interpola tion 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 i n 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 tillageIncrease 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 Iowa 1 

### and then in Indiana 2



### , 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 first

approximation regional increases in SOC due to redu cing 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 Co rn Belt than are the Iowa state curves.Corn/Soybean Rotations in the U.S. Corn Belt



### 19

- 4 West and Post

4 

### 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 9 00 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 Post 4 

### 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 fro m 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/so ybean 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 ( Δ SOCs) 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-tillCorn/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 t

o 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 incen tive 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. Conservatio n

- 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 approximatio n, 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. Monitor ing 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 ar e presented in Table 19

- 3. Fuel usage rates are from Langemeier and Taylor

5 

### . 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 tillage

system effects on GHG emissions 6



### .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 N 2 

### fr om 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 requir ing 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 cor n following corn 7 

### . 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 managed 8,9 

### . Reducing tillage conserves soil moisture. Higher soil moisture levels increase the potential for N leaching lossesCorn/Soybean Rotations in the U.S. Corn Belt



### 19

- 8 via downward movement with soil water, especially in highly perme able 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 mo isture 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 N 2 

### O losses via denitrification). More readil y 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 be low 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 fer tilizers 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 urea

contai ning 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 systems 10 

### . In the Corn Belt, most of the N is applied as anhydrous ammo nia and by necessity is injected well below the soil surface. Injection reduces the potential for denitrification and N tie

up in organic matter in conservation

- tillage systems. In contrast to urea or urea

- ammonium nitrate solution, ammonia volatilizatio n 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 intensi ve

- 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, especi

ally 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 drained 11



### . In addition to N losses, the amount of N released from crop residues and soil organic matter affect s 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 a mount 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 u p 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 t he 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 wel l 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 fert ilizer will be required with conservation tillage than with intensive tillage.
The N fertilizer recommendations for Michigan, Ohio, and Indiana have been integrated into one system 7 

### and are representative of the Corn Belt. The recommended N rate is not ad justed 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 avera ge 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 tilla ge 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
recom mended on fields receiving manure, regardless of tillage system 12



### . Table 19
- 5 Mean N rates for corn in the ten major U.S. corn producing states

-- 1995 13

Intensive

- till with plow Intensive

- till

without plow Mu lch

- 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 8Corn/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 beca use 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 in tensive

- 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 Marland 6 

### as 1.92 and 2.56 kg active ingredient (a.i.)/ha, respectively. West and Marland 6 

### 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 r ate for moderate

- till corn/soybean rotations was estimated from West and Marland 6 

### 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 N 2 

### O Emissions from Soil Background on N 2 

### O emissions from soil was provided earlier, indicating that N 2 

### O 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, b ut 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 N 2 

### O emissions from 141 to 152 kg CE/ha/yr. Del Grosso et al. 15 

### modeled N 2 

### O emissions as affected by switching corn/soybean rotations from intensive

- till to no

- till on a well drain ed soil in Indiana. The same N fertilizer rate was assumedCorn/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, N 2 

### O emissions were sl ightly lower with no

- till than intensive

- till in both a corn/soybean rotation and continuous corn. Mummey et al. 16 

### modeled N 2 

### O 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 N 2 

### O

- N emissions were 3.04 kg/ha for intensive

- till and 3.28 kg/ha for no

till. Taking the difference (0.24 kg N 2 

### O

- N/ha) times 126.4 kg N 2 

### O CE/kg N 2 

### O

- N gives 30.3 kg N 2 

### O CE/ha/yr. This is a larger increase in N 2 

### O 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 drai ned 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 N 2 

### O emissions with knifed N fertilizer applications that ar e common with anhydrous ammonia in the Corn Belt. The Mummey et al. 16 

### estimates of increased N 2 

### O 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 N 2 O emissions for corn/soybe an rotation when changing from intensive
- till to no
- till

Annual kg N 2 O-N/ha kg N 2 O CE/kg N 2 O-N kg N 2 O 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 N 2 O CE /kg N applied kg N 2 O 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 N 2 

### O Emissions

IndianaCorn/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.
Ta ble 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

CO 2 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

N 2 O 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 an d 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) CO 2 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

YearsAvoided 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 YearsCost($ha) Undiscounted Cost Discounted CostCorn/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 sensit ivity 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 f armer, 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 c omparing costs with other CO 2 

### 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 CO 2 

### 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 ValuesCorn/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. Us ing more N fertilizer increases GHG emissions associated with N fertilizer use and also increases N 2 

### O 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 ra tes fall within the recommended rates for intensive

- till and no

- till corn in Kentucky 11



### . 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 emiss ions 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 Δ SOCs, kg C/ha 7221 7221 7221 E (slope fact or) 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 N 2 O (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 N 2 O 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 113Corn/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 addi tional 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 app lication and on N 2 

### O 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 ma nufacture 17 

### and transportation and storage 18 

### . Emissions from N fertilizer application are from Langemeier and Taylor 5 

### . 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 ) 14.4 14.4 12.5 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 forCorn/Soybean Rotations in the U.S. Corn Belt



### 19

- 18 intensive

- till and no

- till corn , respectively. This resulted in an av erage 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 N 2 

### O emissions from soils in accordance with the IPCC default N 2 

### O emission factor for commercial N fertilizer use 19 

### . The IPCC f actor assumes the following:

• Direct N 2 

### O

- N emissions = 1.25 percent of applied N

• Indirect N 2 

### O

- N emissions from: Volatilized NH

3 

### and NOx = 0.1 percent of applied N Leached N = 0.75 percent of applied N

• Total N 2 

### O

- N emission = 2.1 percent of applied N (i.e., 0 .021 kg N 2 

### O

- N/kg applied N)
The IPCC factor is converted to a carbon equivalent (CE) radiative forcing basis as follows:

• 1.57 N 2 

### O/N 2 

### O

- N

• 296 CO 2 

### equivalent/N 2 

### O

• 0.272 C equivalent (CE)/CO 2 

### equivalent

• 1.57 x 296 x .272 CE/N 2 

### O

- N (126.4 CE/N 2 

### O

- N)
Therefore, 1 kg applied N = 0.021 x 126.4 kg CE from N 2 

### O = 2.65 kg CE from N 2 

### O

The overall increase in N 2 

### O emissions due to both switching from intensive

- till to no

- till and increasing N fertilizer use was calculated as shown i n Table 19

- 13. Increased N 2 

### O 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 tilla ge system.Corn/Soybean Rotations in the U.S. Corn Belt



### 19

- 19

Table 19
- 13 Corn/soybean overall increase in N 2 O emissions due to both switching from intensive
- till to no
- till and increasing N fertilizer use

Annual kg N 2 O-N/ha kg N 2 O CE/kg N 2 O-N kg N 2 O 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 N 2 O CE /kg N applied kg N 2 O 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 N 2 

### O 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 cour se of the 100

- year planning horizon, the added N fertilizer use and associated increase in N 2 

### O 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 exam ple 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

CO 2 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

N 2 O 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 equivalen t (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 o n 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) CO 2 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

YearsAvoided 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 YearsCost($ha) Undiscounted Cost Discounted CostCorn/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 c olumn 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 p aid 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 se lected for comparing costs with other CO 2 

### 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 increas ing N fertilizer use

Current Input Variable Values

Calculation Method for Sequestering CO 2 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 ValuesCorn/Soybean Rotations in the U.S. Corn Belt



### 19

- 24 Examples of the large effects of N rate and N 2 

### O 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

- t ill in a corn/soybean rotation in the Corn Belt. No significant change in N 2 

### O 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 N 2 

### O emissions in T

able 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 N 2 

### O 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 N 2 

### O scenario to $113/tonne CE LC GHG avoided for a worst

- case scenario. Differences between the best

- case and worst

- case scenarios regarding amount o f 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 manage d efficiently so that the amount of N fertilizer used and the N 2 

### O emissions are not significantly increased.
Table 19
- 17 Effects of increases in N rate and N 2 O emissions on amounts and costs of avoided GHG vi a switching from intensive
- till to no
- till in a Corn/Soybean rotation

Δ N rate Δ N 2 O 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 avoided 1 /

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 N 2 O emissions due to switching to no

- till.
3 / Base case (see first column, Table 19

- 1 1).
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 Ref erences

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 St ate 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 stor age 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. Su bmitted 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 Marla nd. 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. Nitr ogen 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. Fertilizat ion 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.htmlCorn/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/ag r/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/ah7 12/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 Gross o, 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, Ec osystems, 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 Ac horn, 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

- 1 998. EPA 236

- R

001. p. 5

- 17 to 5

- 19.

### 20

- 1 

# 20 CONTINUOUS CORN IN T HE 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 emissio ns, 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 fo r 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 Con tinuous Corn

State Indiana Indiana Iowa Iowa
Switching from intensive

- till to: No

- till Moderate

till No

- till Moderate

till

Quadratic SOC response curve

Δ SOCs, 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 N 2 O (years 1

- 100), kg CE/ha/y:
Δ Fuel use

- 22.9

- 20.5

- 22.9

- 20.5 Δ Machinery ma int., 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 N 2 O 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 100Continuous 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 Iowa 1 

### and I ndiana 2 

### . 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 i

ncreases 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 hydr ic (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 Iowa 1 

### and then in Indiana 2 

### , results from Indiana were judged to be most representative o f 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 tillageIncrease 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 IndianaContinuous Corn in the U.S. Corn Belt



### 20

- 4 Example sprea dsheet 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 ( Δ SOCs) 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-tillContinuous 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 l east $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 Ince ntives 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 incen

tives are estimated in Appendix E and ranged from $66 to 100/ha/year for switching from intensive

till 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 v ia 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 incent ive 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 ra tes are from Langemeier and Taylor

3 

### . 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 Taylor 3



### .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 manufa cture, 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 bas ed on ratios of tillage

- system fuel use and machinery MTR in a national analysis of tillage

system effects on GHG emissions 4



### .Continuous Corn in the U.S. Corn Belt



### 20

- 7

Table 20
- 4 Reductions in fuel use and associated carbon emissions by switching fro m 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 dec omposing 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 Marlan

d 4 

### as 2.71 and 3.63 kg active ingredient (a.i.)/ha, respectively. West and Marland 4 

### 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 herbic ide rate for moderate

- till corn/soybean rotations was estimated from West and Marland 4 

### 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 ou tput 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 N 2 

### O Emissions from Soil
Background on N 2 

### O emissions from soil was provided earlier, indicating that N 2 

### O 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 soi l 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 anhydro us ammonia was the primary N source. Switching to no

- till increased N 2 

### O emissions from 141 to 152 kg CE/ha/yr. Del Grosso et al. 6 

### modeled N 2 

### O 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, N 2 

### O emissions were slightly lower with no

- till than intensive

- ti ll in both a corn/soybean rotation and continuous corn. Mummey et al. 7 

### modeled N 2 

### O 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 N 2 

### O

- N emissions were 3.04 kg/ha for intensive

- till and 3.28 kg/ha for no

- till. Taking the difference (0.24 kg N 2 

### O

- N/ha) times 126.4 kg N 2 

### O CE/kg N 2 

### O

- N gives 30.3 kg N 2 

### O CE/ha/yr. This is a larger increase in N 2 

### O emissions due to switching to no

- till than was found by Robertson et al. 5Continuous 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 cov

ered a range of soil and environmental conditions. On the other hand, Mummey et al. 7 

### did not account for possible reductions in denitrification and N 2 

### O emissions with knifed N fertilizer applications that are common with anhydrous ammonia in the Corn Belt . The Mummey et al. 7 

### estimates of increased N 2 

### O 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 20.1 for the continuous corn Corn Belt base case. Example spreadsheet out put is presented in Table 20

- 6.
Table 20
- 6 Example spreadsheet output for carbon equivalent emissions from changes in N 2 O emissions for continuous corn when changing from intensive
- till to no
- till

Annual kg N 2 O-N/ha kg N 2 O CE/kg N 2 O-N kg N 2 O 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 N 2 O CE /kg N applied kg N 2 O 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 N 2 O Emissions

IndianaContinuous 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 spre adsheet 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

CO 2 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

N 2 O 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 presen ted 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 intens ive
- 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) CO 2 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

YearsAvoided 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
YearsCost($ha) Undiscounted Cost Discounted CostContinuous 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 ba ck 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 CO 2 

### 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 CO 2 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 IndianaContinuous 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 N 2 

### O 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 Kentucky 8



### . 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 Δ SOCs, 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 N 2 O (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 N 2 O 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), t onne 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 129Continuous 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 monitor ing 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 ton nes 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, transportat ion, storage, and application and on N 2 

### O 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 manufacture 9 

### and transportation and storage 10 

### . Emissions from N fertilizer application are from Langemeier and Taylor 3 

### . The manufacturing numbers assume that all energy is from natural gas, but a small undermined amount of energy is from elec tricity. 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 placeme nt

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

IndianaContinuous Corn in the U.S. Corn Belt



### 20

- 1 6 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 fertilize r use of 25 kg N/ha.

The increase in N fertilizer use with no

- till was assumed to increase N 2 

### O emissions from soils in accordance with the IPCC default N 2 

### O emission factor for commercial N fertilizer use 11 

### . The IPCC factor assumes the following:

• Direct N 2 

### O

- N emissions = 1.25 percent of applied N

• Indirect N 2 

### O

- N emissions from: Volatilized NH

3 

### and NOx = 0.1 percent of applied N Leached N = 0.75 percent of applied N

• Total N 2 

### O

- N emission = 2.1 percent of applied N (i.e., 0.021 kg N 2 

### O

- N/kg applied N)

The IPCC f

actor is converted to a carbon equivalent (CE) radiative forcing basis as follows:

• 1.57 N 2 

### O/N 2 

### O

- N

• 296 CO 2 

### equivalent/N 2 

### O

• 0.272 C equivalent (CE)/CO 2 

### equivalent

• 1.57 x 296 x .272 CE/N 2 

### O

- N (126.4 CE/N 2 

### O

- N)
Therefore, 1 kg applied N = 0.021 x 126.4 kg CE fro m N 2 

### O = 2.65 kg CE from N 2 

### O

The overall increase in N 2 

### O 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 N 2 

### O emission s 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 N 2 O emissions due to both switching from intensive
till to no
- till and increasing N fertilizer use

Annual kg N 2 O-N/ha kg N 2 O CE/kg N 2 O-N kg N 2 O 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 N 2 O CE /kg N applied kg N 2 O 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 N 2 O Emissions

Indiana



### 20.3.2 Summary of Avoided GHG Emissions Example spreadsheet output summarizing cha nges 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 horizo n, the added N fertilizer use and associated increase in N 2 

### O 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 summarizi ng 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

CO 2 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

N 2 O 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 Ta ble 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) CO 2 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

YearsAvoided 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
YearsCost($ha) Undiscounted Cost Discounted CostContinuous 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 incentiv e 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 inten sive

- 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 CO 2 

### sink enhancement or stor age 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 CO 2 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 IndianaContinuous Corn in the U.S. Corn Belt



### 20

- 22 Examples of the large effects of N rat e and N 2 

### O 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 i n N 2 

### O 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 N 2 

### O emissions in Table 20

- 16 due to switching from intensive

- till to no

- till are possibilit ies 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 N 2 

### O 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 av oided with a best

- case N rate and N 2 

### O 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 o ver 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 N 2 

### O emissions ar e not significantly increased.
Table 20
- 16 Effects of increases in N rate and N 2 O emissions on amounts and costs of avoided GHG via switching from intensive
- till to no
- Continuous Corn

Δ N rate Δ N 2 O 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 avoided 1 /

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 i ncentive payments for 20 years.

2 / Base case except for no change in N 2 O 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 colu mn, 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. Kil lian, 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 Ecol ogy 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 gree nhouse 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 c arbon 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. Greenho use 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

- R

001. 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 l ower than for con/soybean rotations or continuous corn in the Corn Belt. On the other hand, reducing tillage on wheat/fallow rotations either reduces N 2 

### O emissions or has no significant effect on N 2 

### O emissions and a smaller adoption incentive is required to get farmers to switch from intensive

- till to no

- till. The net effect is that CO 2 

### 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 N 2 

### O emissions due to reducing tillage and the sensitivity cases (Section 21.3) assume typical levels of carbon sequestration and no change in N 2 

### O emissions due to reducing tillage.


## 21.2 Wheat/Fallow (Base Cas es)
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 dis cussed in the following sections using output from the wheat/fallow spreadsheet model developed for this project. Base cases assuming a reduction in N 2 

### O emissions due to reducing tillage are presented in the first two columns of Table 21

- 1.

21.2.1 Increas es in Soil Organic Carbon Using the same SOC model for wheat/fallow rotations in Nebraska 1 

### as for corn/soybean rotations and continuous corn in Iowa 2 

### and Indiana 3 

### , 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 switchin g 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 sw itching from intensive

- till to no

- till is to use coefficients formulated by the IPCC 4 

### . 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 o f the drier western portion of the Great Plains with predominantly wheat/fallow rotations, switching from intensive

- till to no

- till resulted in anWheat/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 SOC 5 

### . A model simulation 6 

### 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 fo r 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 CO 2 

### sink enhancement or storage options.
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
Linear SOC response curve Base

- case Δ N 2 O Zero Δ N 2 O 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 N 2 O (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 N 2 O 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 167Wheat/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 adopt ion incentives is overviewed in Appendix E and an adoption incentive of $15/ha/year is estimated for switching from intensive

till to no

- till in wheat/fallow rotations in the western Great Plains. Transaction costs are based on experience with forestry pr ojects 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

measureme nts 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 Taylor

7



### . Table 21
- 3 Reductions in fuel use and associated carbon emissions by switching from intensive
- til l 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/fa llow rotations were calculated as 18 percent of the fuel savings (Tables 21

- 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 emissions 8



### .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 assumed 9 

### . 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 communic ation, 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/fa llow 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 N 2 

### O Emissions from Soil Background on N 2 

### O emissions from soil was provided earlier, indicating that N 2 

### O emissions are dependent on both soil conditions and amount of N fertilizer applied. In this western Great Plains wheat/fallo w 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 N 2 

### O 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, N 2 

### O emissions were slightly lowe r with no

- till than intensive

- till. Mummey et al. 10 

### modeled N 2 

### O 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 sy stems represent a significant portion of the wheat producing area. This modeling effort estimated mean N 2 

### O

- N emissions of 4.8 kg/ha/year for intensive

- till andWheat/Fallow in the Western Great Plains



### 21

- 6 4.6 kg/ha/year for no

- till. Taking the difference (

- 0.2 kg N 2 

### O

- N/ha/year) times 126.4 kg N 2 

### O C E/kg N 2 

### O

- N gives

- 25.3 kg N 2 

### O 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 equi valent emissions from changes in N 2 O emissions for wheat/fallow rotation when changing from intensive

- till to no
- till

Annual kg N 2 O-N/ha kg N 2 O CE/kg N 2 O-N kg N 2 O 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 N 2 O CE /kg N applied kg N 2 O 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 N 2 O 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

CO 2 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

N 2 O 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) CO 2 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
YearsAvoided 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 YearsCost($/ha) Undiscounted Cost Discounted CostWheat/Fallow in the Western Great Plains



### 21

- 9

21.2.5 Sensitivity Summary
Example spreadsheet sen sitivity 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 hori zon 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 b ase case of incentive payments for 10 years was selected for comparing costs with other CO 2 

### 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 CO 2 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 ValuesWheat/Fallow in the Western Great Plains



### 21

- 11



## 21.3 Wheat/Fallow (Sens itivity Cases with No Tillage Effect on N 2 

## O Emissions)

These cases are the same as the western Great Plains wheat/fallow base cases in Section 21.2 except that no change in N 2 

### O emissions is assumed as a result of reducing tillage. These cases correspond t

o columns 3 and 4 of Table 21

- 1. Assuming no decrease in N 2 

### O emissions due to switching from intensive

- till to no

- till results in somewhat higher costs of CO 2 

### abatement ($84 vs. $71/tonne CE LC avoided in the 200 kg SOC/ha/year case and $167 vs. $123/tonn e 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 N 2 

### O 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 N 2 O emissions

Changes in GHG Emissions
kg CE/ha/yr Average Soil Organic Carbon (SOC) sequestered ( years 1

- 20 ) -200

CO 2 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

N 2 O 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 PlainsWheat/Fallow in the Western Great Plains



### 21

- 12 21.3.2 Summary
Example spr eadsheet 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 rat e of 70 kg/ha/yr. A base case of incentive payments for 10 years was selected for comparing costs with other CO 2 

### sink enhancement or storage options.Wheat/Fallow in the Western Great Plains



### 21

- 13

Table 21
- 10 Wheat/fallow costs and life

- cycle (LC) avoide d GHG on a carbon equivalent (CE) basis when changing from intensive
- till to no
- till with no change in N 2 O 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) CO 2 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
YearsAvoided 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 YearsCost($/ha) Undiscounted Cost Discounted CostWheat/Fallow in the Western Great Plains



### 21

- 14 Table 21
- 11 Wheat/fallow example spreadsheet sensitivity summary output when changing fr om intensive
- till to no
- till with no change in N 2 O emissions

Current Input Variable Values

Calculation Method for Sequestering CO 2 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 ValuesWheat/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 Partner

ship. 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 a nd 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 Labor

atory 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 contermin ous 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 Jo urnal.

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. Hans en (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 Agricul tural 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. Assessmen t 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 SOUTHE ASTERN UNITED STATES

22.1 Introduction
Cotton is grown primarily in the southeastern United States. Switching from intensive

- till to no

till cotton results in somewhat greater increases in SOC than in wheat/fallow systems, primarily b ecause 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 increase s in N 2 

### O emissions in contrast to the slight decreases in N 2 

### O 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 abov

e factors is that CO 2 

### 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 N 2 

### O emissions due to reducing til lage and the sensitivity cases (Section 22.3) assume typical levels of carbon sequestration and no change in N 2 

### O 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 emissi ons, 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. Bas e cases assuming an increase in N 2 

### O 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

- till 1 

### . Using IPPC coefficients for the southeastern U.S., switching from intensive

- till to no

- till resul ted 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

- till 2 

### . Cotton is a significant component of the cropping systems in the southeastern U.S. Using IPPC coefficients for t he 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 switchi ng from intensive

- till to no

- till 2 

### . 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 andCotton 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 CO 2 

### sink enhancement or storage options.
Table 22
- 1 Effects of swi tching 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
Linear SOC response curve Base

- case Δ N 2 O Zero Δ N 2 O kg C/ha/yr 300 150 300 150 Year s to new steady state (Ys) 20 20 20 20

GHG emissions from tillage
- system inputs and N 2 O (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 N 2 O 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 160Cotton 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 i ncentive of $25/ha/year is estimated for switching from intensive

till 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 fo r 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 a nd Taylor 3



### . 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

0.94Cotton 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 tillag e

- system fuel use and machinery MTR in a national analysis of tillage

- system effects on GHG emissions 4



### .

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 inte nsive

- 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. Inte nsive

- 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 N 2 

### O Emissions from Soil Background on N 2 

### O emissions from soil was provided earlier, indicating that N 2 

### O 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 N 2 

### O emissions. Mummey et al. 5 

### modeled N 2 

### O emissions for intensive

- till and no

till 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 N 2 

### O

- N emissions ofCotton 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 N 2 

### O

- N/ha/year) times 126.4 kg N 2 

### O CE/kg N 2 

### O

- N gives 63.2 kg N 2 

### O 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 N 2 O emissions for cotton when changing from intensive
- till to no
- till

Annual kg N 2 O-N/ha kg N 2 O CE/kg N 2 O-N kg N 2 O 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 N 2 O CE /kg N applied kg N 2 O 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 N 2 O 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

CO 2 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

N 2 O 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) CO 2 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

YearsAvoided 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 YearsCost($/ha) Undiscounted Cost Discounted CostCotton in the Southeastern United States



### 22

- 9

22.2.5 Sensitivity Summary
Example sp readsheet 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 CO 2 

### 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 CO 2 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 StatesCotton in the Southeastern United States



### 22

- 11



## 22.3 Cot ton (Sensitivity Cases with No Tillage Effect on N 2 

## O Emissions)
These cases are the same as the cotton base cases in Section 22.2 except that no change in N 2 

### O 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 N 2 

### O emissions due to switching from intensive

- till to

no

- till results in significantly lower costs of CO 2 

### abatement ($132 vs. $91/tonne CE LC avoided in the 300 kg SOC/ha/year case and $350 vs. $160/tonne CE LC avoid ed 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
Examp le spreadsheet output summarizing changes in GHG emissions due to switching from intensive

- till to no

- till cotton with no change in N 2 

### O 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 N 2 O emissions

Changes in GHG Emissions
kg CE/ha/yr Average Soil Organic Carbon (SOC) sequestered ( years 1

- 15 ) -300

CO 2 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

N 2 O 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 StatesCotton 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. T his 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 CO 2 

### 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 N 2 O 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) CO 2 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 YearsAvoided 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 YearsCost($/ha) Undiscounted Cost Discounted CostCotton 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 N 2 O emissions

Current Input Variable Values

Calculation Method for Sequestering CO 2 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 StatesCotton 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. S perow, 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 CO 2 

### capture, transport and storage and indirect capture by forests and cropland via reducing tillage using the costs of captu re from another EPRI study 1



### . 

## 23.1 Transport and Storage

Shown in Table 23

- 1 is a comparison of the transport and storage costs of captured CO 2 

### developed in this study on a $/tonne of CO 2 

### and $/tonne of C on a levelized annual equivalent life cycle greenhou

se 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 f rom the previous EPRI study which only compressed the CO 2 

### to 83 bar (1200 psia) rather than the 153 bar (2200 psia) required at the pip e line inlet. This difference represents a small fraction of the costs of CO 2 

### transport and storage ($0.75/tonne of CO 2 

### o r $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. Plo tted in Figure 23

- 1 are the values on a CO 2 

### basis to visually represent the comparisons.
Table 23
- 1 CO 2 transport and storage cost comparison on a levelized annual CO 2 equivalent life cycle greenhouse gas avoi ded basis

Case Base Case High Cost Case Low Cost

Case Base

Case High

Cost Case Low

Cost Case $/tonne CO 2 $/tonne

CO 2 $/tonne

CO 2 $/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 58Summary 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 CO2Eq. LC GHG Avoided

Figure 23
- 1 CO 2 transport and storage cost comparison on a levelized annual $/t of CO 2 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 CO 2 

### , 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, o il, 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 devel oped in this study on a $/tonne of CO 2 

### 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 CO 2 

### information is shown in Figure 23.2.Summary Comparisons and Conclusions



### 23

- 3

Table 23
- 2 CO 2 capture, transport and storage cost comparison on a NPV equivalent life cycle greenhouse gas avoided basis

Case Base Case Base Case $/tonne CO 2 $/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 CO2Eq. LC GHG Avoided

Figure 23
- 2 CO 2 capture, transport and storage cost comparison on a NPV $/t of CO 2 equivalent life cycle greenhouse gas avoided basisSummary Comparisons and Conclusions



### 23

- 4 

## 2 3.3 Forest management

Shown in Table 23

- 3 is a comparison of the forestry cases developed in this study on a $/tonne of CO 2 

### and $/tonne of C on a NPV life cycle greenhouse gas avoided basis. Sensitivity to productivity are indicated where data was availa ble 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 equi valent 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

- Hi gh Productivity High Cost

Case

- Low Productivity $/tonne CO 2 $/tonne CO 2 $/tonne CO 2 $/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) (8 7) 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 CO 2 

### costs from Table 26.2 to visually represent t he 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 CO2Eq. LC GHG Avoided

Above + Below ground + products + revenues

Figure 23
- 3 Forestry cost comparison on a NPV $/t of CO 2 equivalent life cycle greenhouse gas avoided basis


### The only case with a significant cost is the US pine case and this is because th e 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 CO 2 

### and $/tonne of C on a NPV equivalent life cycle greenhouse gas avoided basis rounded off to the nea rest dollar

. A plot of the $/t CO 2 

### 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 I ncentive Low Cost

Case

- 5 y Incentive Base

Case

- 10 y incentive High Cost

Case

- 20 y Incentive Low Cost

Case

- 5 y Incentive $/tonne CO 2 $/tonne

CO 2 $/tonne

CO 2 $/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 CO2Eq. LC GHG Avoided

Figure 23
- 4 Cropland cases NPV $/t CO 2 equivalent life cycle greenhouse gas avoided basis


### All of these optio ns 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 o f the base case values in $/tonne of CO 2 

### 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 CO2Eq. 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 c ases 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 CO2Eq. LC GHG Avoided Base Case High Case
Low Case

Figure 23
- 6 Comparison of di rect 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 CO 2 

### capture and storage options are presented in Table 23

- 5. These land area equivalents are f or a 100

- year planning horizon. The carbon equivalent (CE) LC GHG emissions avoided by the capture and storage options assume CO 2 

### capture and injection into storage reservoirs during the 20

- year book life of the capture and storage plants and storage of i njected CO 2 

### for another 80 years. For the ocean storage options, the CE LC GHG avoided is reduced for leakage of CO 2 

### throughout the 100

- year storage period. Zero CO 2 

### leakage is assumed for the other CO 2 

### storage options. The CE LC GHG emissions avoided b y 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 CO 2 

### 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 CO 2 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 CO 2 Capture & Storage CO 2 Capture & Storage CO 2 Capture & Storage CO 2 Capture & Storage CO 2 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 i ndirect 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 b e 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. Dep artment of Energy/NETL, Pittsburgh, Pennsylvania: 1000316.

### A

- 1 

# A
DETAILED LIFE
- CYCLE GHG EMISSIONS AVOIDED VIA CAPTURING AND ST ORING CO 2



### Detailed life

- cycle (LC) GHG emissions avoided via capturing and storing CO 2 

### in the base cases considered in this project are presented in Tables A

- 1 to A

- 7. Methodology for calculat ing these emissions is described in Chapters 2 and 3.Detailed Life

- Cycle GHG Emissions Avoided Via Capturing and Storing CO 2



### 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 CO 2

Capture (Case 3a) Without CO 2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N 2 0 CH 4 CO 2 N 2 0 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 CO 2 Equivalent 243,338 3,433 66,762 Total Power Plant CO 2 Equivalent 2,167,749 3,530 60,262 Total (mg CO 2

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 CO 2 to Pipeline Spec CO 2 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 CO 2 Equivalent 1,605 23 438 CO 2 Compression Total ( CO 2 Equivalent ) 2,066
Transportation & Sequestration of Captured CO 2 CO 2 Transportation

Pipeline Construction 913 0.0 0.0 Pipeline Decommissioning 91 0.0 0.0 Total 1,005 0 0 CO 2 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 CO 2 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 CO

2 equivalent emitted/kWh 0.111 0.750 0.639 Compression CO 2 equivalent emitted, tonne/yr 2,066 CO 2 equivalent avoided, tonne/yr 1,807,446 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 133,538 Total CO

2 equivalent emitted, tonne/yr 135,604 Total System CO 2 equivalent avoided by capture, tonne/yr 1,807,446 CO 2 equivalent emitted from storage, tonne/yr 135,604 CO 2 equivalent LC avoided, tonne/yr 1,671,842 CO 2 equivalent LC avoided, % of CO 2 captured 77.5% CO 2 equivalent LC avoided, % of CO 2 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 & StorageDetailed Life

- Cycle GHG Emissions Avoided Via Capturing and Storing CO 2



### A

- 3 Table A
- 2 ECBMR base case detail ed 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 CO 2

Capture (Case 3a) Without CO 2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N 2 0 CH 4 CO 2 N 2 0 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 CO 2 Equivalent 243,338 3,433 66,762 Total Power Plant CO 2 Equivalent 2,167,749 3,530 60,262 Total (mg CO 2

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 CO 2 to Pipeline Spec CO 2 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 CO 2 Equivalent 1,605 23 438 CO 2 Compression Total ( CO 2 Equivalent ) 2,066
Transportation & Sequestration of Captured CO 2 CO 2 Transportation

Pipeline Construction 913 0.0 0.0 Pipeline Decommissioning 91 0.0 0.0 Total 1,005 0 0 CO 2 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 CO 2 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 CO 2 equivalent emitted, tonne/yr 2,066 kg CO

2 equivalent emitted/kWh 0.111 0.750 0.639 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 49,798 CO 2 equivalent avoided, tonne/yr 1,807,446 Total CO 2 equivalent emitted, tonne/yr 51,864
Total System CO 2 equivalent avoided by capture, tonne/yr 1,807,446 CO 2 equivalent emitted from storage, tonne/yr 51,864 CO 2 equivalent LC avoided, tonne/yr 1,755,582 CO 2 equivalent LC avoided, % of CO 2 captured 81.4% CO 2 equivalent LC avoided, % of CO 2 equivalent avoided during capture 97.1%

GHG (CO

2 Equivalents) Emmited During CO 2 Capture & Storage Enhanced Coal Bed Methane: Greenhouse Gas Life-Cycle (LC) Analysis

GHG Emissions tonnes/year GHG Emissions tonnes/yearDetailed Life

- Cycle GHG Emi ssions Avoided Via Capturing and Storing CO 2



### 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 CO 2

Capture (Case 3a) Without CO 2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N 2 0 CH 4 CO 2 N 2 0 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 CO 2 Equivalent 243,338 3,433 66,762 Total Power Plant CO 2 Equivalent 2,167,749 3,530 60,262 Total (mg CO 2

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 CO 2 to Pipeline Spec CO 2 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 CO 2 Equivalent 1,605 23 438 CO 2 Compression Total ( CO 2 Equivalent ) 2,066
Transportation & Sequestration of Captured CO 2 CO 2 Transportation

Pipeline Construction 913 0.0 0.0 Pipeline Decommissioning 91 0.0 0.0 Total 1,005 0 0 CO 2 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 CO 2 Equivalent 1,008 0 0 Transportation & Sequestration Total ( CO 2 Equivalent ) 1,008

Amount of Emitted & Avoided CO 2

With Without Capture CO2 Capture CO2 Capture Difference Storage (CO2 Compression, Transportation, and Sequestration) kg CO

2 equivalent emitted/kWh 0.111 0.750 0.639 Compression CO 2 equivalent emitted, tonne/yr 2,066 CO 2 equivalent avoided, tonne/yr 1,807,446 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 1,008 Total CO

2 equivalent emitted, tonne/yr 3,074 Total System CO 2 equivalent avoided by capture, tonne/yr 1,807,446 CO 2 equivalent emitted from storage, tonne/yr 3,074 CO 2 equivalent LC avoided, tonne/yr 1,804,372 CO 2 equivalent LC avoided, % of CO 2 captured 83.6% CO 2 equivalent LC avoided, % of CO 2 equivalent avoided during capture 99.8%

Aquifer Storage: Greenhouse Gas Life-Cycle (LC) Analysis

GHG Emissions tonnes/year GHG Emissions tonnes/yearDetailed Life

- Cycle GHG Emissions Avoided Via Capturing and Storing CO 2



### A

- 5 Table A
- 4 Depleted oil reservo ir 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 CO 2

Capture (Case 3a) Without CO 2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N 2 0 CH 4 CO 2 N 2 0 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 CO 2 Equivalent 243,338 3,433 66,762 Total Power Plant CO 2 Equivalent 2,167,749 3,530 60,262 Total (mg CO 2

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 CO 2 to Pipeline Spec CO 2 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 CO 2 Equivalent 1,605 23 438 CO 2 Compression Total ( CO 2 Equivalent ) 2,066
Transportation & Sequestration of Captured CO 2 CO 2 Transportation

Pipeline Construction 913 0.0 0.0 Pipeline Decommissioning 91 0.0 0.0 Total 1,005 0 0 CO 2 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 CO 2 Equivalent 1,023 0 0 Transportation & Sequestration Total ( CO 2 Equivalent ) 1,024

Amount of Emitted & Avoided CO 2

With Without Capture CO2 Capture CO2 Capture Difference Storage (CO2 Compression, Transportation, and Sequestration) kg CO

2 equivalent emitted/kWh 0.111 0.750 0.639 Compression CO 2 equivalent emitted, tonne/yr 2,066 CO 2 equivalent avoided, tonne/yr 1,807,446 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 1,024 Total CO

2 equivalent emitted, tonne/yr 3,089 Total System CO 2 equivalent avoided by capture, tonne/yr 1,807,446 CO 2 equivalent emitted from storage, tonne/yr 3,089 CO 2 equivalent LC avoided, tonne/yr 1,804,356 CO 2 equivalent LC avoided, % of CO 2 captured 83.6% CO 2 equivalent LC avoided, % of CO 2 equivalent avoided during capture 99.8%

Depleted Oil Reservoir: Greenhouse Gas Life-Cycle (LC) Analysis

GHG Emissions tonnes/year GHG Emissions tonnes/yearDetailed Life

- Cycle GHG Emi ssions Avoided Via Capturing and Storing CO 2



### 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 CO 2

Capture (Case 3a) Without CO 2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N 2 0 CH 4 CO 2 N 2 0 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 CO 2 Equivalent 243,338 3,433 66,762 Total Power Plant CO 2 Equivalent 2,167,749 3,530 60,262 Total (mg CO 2

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 CO 2 to Pipeline Spec CO 2 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 CO 2 Equivalent 1,605 23 438 CO 2 Compression Total ( CO 2 Equivalent ) 2,066
Transportation & Sequestration of Captured CO 2 CO 2 Transportation

Pipeline Construction 913 0.0 0.0 Pipeline Decommissioning 91 0.0 0.0 Total 1,005 0 0 CO 2 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 CO 2 Equivalent 1,041 0 0 Transportation & Sequestration Total ( CO 2 Equivalent ) 1,041

Amount of Emitted & Avoided CO 2

With Without Capture CO2 Capture CO2 Capture Difference Storage (CO2 Compression, Transportation, and Sequestration) kg CO

2 equivalent emitted/kWh 0.111 0.750 0.639 Compression CO 2 equivalent emitted, tonne/yr 2,066 CO 2 equivalent avoided, tonne/yr 1,807,446 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 1,041 Total CO

2 equivalent emitted, tonne/yr 3,107 Total System CO 2 equivalent avoided by capture, tonne/yr 1,807,446 CO 2 equivalent emitted from storage, tonne/yr 3,107 CO 2 equivalent LC avoided, tonne/yr 1,804,339 CO 2 equivalent LC avoided, % of CO 2 captured 83.6% CO 2 equivalent LC avoided, % of CO 2 equivalent avoided during capture 99.8%

Depleted Gas Reservoir: Greenhouse Gas Life-Cycle (LC) Analysis

GHG Emissions tonnes/year GHG Emissions tonnes/yearDetailed Life

- Cycle GHG Emissions Avoided Via Capturing and Storing CO 2



### 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 CO 2 Capture (Case 3a) Without CO 2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N 2 0 CH 4 CO 2 N 2 0 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 CO 2 Equivalent 243,338 3,433 66,762 Total Power Plant CO 2 Equivalent 2,167,749 3,530 60,262 Total (mg CO 2

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 CO 2 to Pipeline Spec CO 2 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 CO 2 Equivalent 4,814 68 1,315 CO 2 Compression Total ( CO 2 Equivalent ) 6,197
Transportation & Sequestration of Captured CO 2 CO 2 Transportation

Pipeline Construction 913 0.0 0.0 Pipeline Decommissioning 91 0.0 0.0 Total 1,005 0 0 CO 2 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 CO 2 Equivalent 5,702 48 941 Transportation & Sequestration Total ( CO 2 Equivalent ) 6,692

Amount of Emitted & Avoided GHG (CO 2 Equivalents)
With Without Capture CO2 Capture CO2 Capture Difference kg CO

2 equivalent emitted/kWh 0.111 0.750 0.639 Storage (CO2 Compression, Transportation, and Sequestration) CO 2 equivalent avoided for a single IGCC plant, tonne/yr 1,807,446 Compression CO 2 equivalent emitted, tonne/yr 6,197 Number of IGCC plants transporting CO 2 to ocean site 3 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 6,692 Total CO

2 equivalent avoided, tonne/yr 5,422,337 Total CO 2 equivalent emitted, tonne/yr 12,889
Total System CO 2 equivalent avoided by capture, tonne/yr 5,422,337 CO 2 equivalent emitted from storage, tonne/yr 12,889 CO 2 equivalent LC avoided w/o leakage, tonne/yr 5,409,447 CO 2 equivalent LC avoided w/o leakage, % of CO 2 captured 83.6% CO 2 equivalent LC avoided w/o leakage, % of CO 2 equivalent avoided during capture 99.8%

GHG Emissions GHG Emissions tonnes/year tonnes/year GHG (CO 2

Equivalents) Emmited During CO 2 Capture & Storage Ocean Pipeline Storage: Greenhouse Gas Life-Cycle (LC) Analysis (without leakage)Detailed Life

- Cycle GHG Emi ssions Avoided Via Capturing and Storing CO 2



### 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 CO 2 Capture (Case 3a) Without CO 2 Capture (Case 3b)

IGCC Plant IGCC Plant

CO 2 N 2 0 CH 4 CO 2 N 2 0 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 CO 2 Equivalent 243,338 3,433 66,762 Total Power Plant CO 2 Equivalent 2,167,749 3,530 60,262 Total (mg CO 2

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 CO 2 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 CO 2 Equivalent 4,814 68 1,315 CO 2 Compression Total ( CO 2 Equivalent ) 6,197
Transportation & Sequestration of Captured CO 2 CO 2 Transportation

Pipeline Construction 913 0.0 0.0 Pipeline Decommissioning 91 0.0 0.0 Total 1,005 0 0 CO 2 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 CO 2 Equivalent 57,899 2 386 Transportation & Sequestration Total ( CO 2 Equivalent ) 58,287

Amount of Emitted & Avoided GHG (CO 2 Equivalents)
With Without Capture CO2 Capture CO2 Capture Difference kg CO

2 equivalent emitted/kWh 0.111 0.750 0.639 Storage (CO2 Compression, Transportation, and Sequestration) CO 2 equivalent avoided, tonne/yr 1,807,446 Compression CO 2 equivalent emitted, tonne/yr 6,197 Number of IGCC plants transporting CO 2 to ocean site 3 Transportation and sequestration CO 2 equivalent emitted, tonne/yr 58,287 Total CO

2 equivalent avoided, tonne/yr 5,422,337 Total CO 2 equivalent emitted, tonne/yr 64,484
Total System CO 2 equivalent avoided by capture, tonne/yr 5,422,337 CO 2 equivalent emitted from storage, tonne/yr 64,484 CO 2 equivalent LC avoided, tonne/yr 5,357,852 CO 2 equivalent LC avoided, % of CO 2 captured 82.8% CO 2 equivalent LC avoided, % of CO 2 equivalent avoided during capture 98.8% GHG Emissions GHG Emissions tonnes/year tonnes/year GHG (CO 2

Equivalents) Emmited During CO 2 Capture & Storage Ocean Tanker Storage: Greenhouse Gas Life-Cycle (LC) Analysis (without leakage)

### B

- 1 

# B EXPLANATION OF PROCE SS INPUT AND SUMMARY SPREADSHEETS

Process Input sheet

The Process Input

sheet includes key physical and economic parameters for estimating CO 2 

### storage costs. As for the entire spreadsheet, parameters in blue and contained in rectangul ar 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 spread sheet. The design basis assumes that one IGCC (Case 3a) CO 2 

### capture plant provides the CO 2 

### 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 C O 2 

### 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 CO 2 

### storage costs. In the case of the other storage options, howev er, there is no value

- added product.

EOR and ECBMR Storage Options Only

The economic section of the P rocess I nput sheet is structured the same for the EOR and ECBMR options. Therefore, the description that is given below, which directly relates to the E OR 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 CO 2 

### storage c ost minus enhanced oil revenue gives the net cost of storing CO 2 

### . When oil revenue is greater than the CO 2 

### storage cost, the net cost of storing CO 2 

### is negative (i.e., a revenue). From the perspective of an EOR operation, a positive net storage cost equa ls the breakeven CO 2 

### tipping fee. A negative net storage cost equals the breakeven CO 2 

### purchase price. Note that a Breakeven Oil Price is provided in the Process Input sheet. This is theExplanation of Process Input and Summary Spreadsheets



### B

- 2

Wellhead Oil Price required for oil revenues to offset the cost of CO 2 

### s torage. 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 CO 2 

### revenues (i.e., CO 2 

### price times tonnes CO 2 

### abated) and CO 2 

### abatement costs to reflect the time

- value of money. For regulated economics, the return on debt and equity is set by reg ulators 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 regulate d 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 sect ions in this sheet summarize the cost of capturing and storing CO 2 

### . The bottom

- line costs in Summary sheet sections 1

- 3 are in units of $/tonne CO 2 

### captured or avoided. Costs in Summary sheet section 4 are in units of $/MWh and indicate incremental effec ts of CO 2 

### capture, CO 2 

### storage, and Year 21

- 100 storage costs on cost of electricity (COE).

Summary Sheet Section 1

— Levelized Annual CO 2 

### Storage Costs (

Y ears 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 CO 2 

### Captured Basis (column 1) and a Life

Cycle (LC) Avoided GHG Basis for storage only (column 3). General approaches for calculating costs on thes e bases are described on in Sections 2.2 and 2.3.

Summary Sheet Section 2

— Levelized Annual CO 2 

### Capture and Net Storage Costs ( Y ears 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 CO 2 

### Captured Basis (column 1), CO 2 

### 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

— CO 2 

### Capture and Storage Costs, NPV Basis ( Y ears 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 CO 2 

### ; these bottom

- line costs are calculated as the breakeven CO 2 

### price requir ed to offset CO 2 

### capture and storage costs. Note that this breakeven CO 2 

### price is constant over time. This is the price at which the NPV of CO 2 

### revenues (i.e., CO 2 

### price times CO 2 

### stored or avoided) equals the NPV of costs for CO 2 

### capture and storage com bined. The theory for calculating the breakeven CO 2 

### price is presented in Section 2.4. The same theory and procedures are used for the forestry and cropland CO 2 

### sink enhancement options in this project. Using these procedures, the bottom

- line costs ($/t onne CO 2 

### equivalent of GHG avoided on a life

- cycle basis) in c olumn 3 of Summary sheet section 3 can be compared with life

- cycle costs for forestry and cropland sink enhancement options that differ greatly from CO 2 

### capture/storage options in the timing of when costs occur and when CO 2 

### is stored and/or leaks back to the atmosphere.
Equation (8) in Section 2.4 reduces to a ratio of total CO 2 

### abatement costs to total CO 2 

### abatement for cases in which annual CO 2 

### storage costs and annual CO 2 

### abatement change pro portionally over time. For the CO 2 

### capture and storage options in this project, CO 2 

### abatement costs and CO 2 

### abatement are levelized over time for years 1

- 20. This means that the bottom

- line costs in Summary sheet section 3 ($/tonne of CO 2 

### ) are the same a s in Summary sheet section 2 unless monitoring and/or transaction costs occur in years 21

- 100 without corresponding levels of CO 2 

### abatement. The difference in bottom

- line costs for Summary

sheet sections 2 and 3 is also increased if CO 2 

### leaks occur in yea rs 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 CO 2 

### capture (years 1

- 20), CO 2 

### 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 PROJEC TS

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 gree nhouse 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 19 92 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 s equestration 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 Activi ties 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 Cost s 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 Re duced 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 protec tion, 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 F orestry 19 97 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 forestryStatus and Costs of Existing Forestry Sequestration Projects



### C

- 3

Table C

- 1 (continued) Forestry sequestration projects in itiated 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 rehabili tation 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 Fores t rehabilitation and management TNC Guaraquecaba 1999 n a N a. Brazil USA Forest conservation, rehabilitation and management Totals/average 104,541 4,239,930Status and Cost s of Existing Forestry Sequestration Projects



### C

- 4 

## References

1 Moura

Costa P. Forestry

- based greenhouse gas mitigation: a short history o f market evolution

. Global Greenhouse Emissions Trader Issue 8, Sept. 2000, p

. 2

- 3.

### D

- 1 

# D PUBLISHED COSTS OF F ORESTRY 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 plan ting 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 S outh 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)Publis hed 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, pub lic 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 Past ure 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

. Dec ember

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. a l

. 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 Aus tralian 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 st eam cycle with offset forestry CO 2 

### mitigation

. R eport produced for the IEA Greenhouse Gas R&D Programme, Oct ober 1994.

7 Huotari, J. , S. Helynen , and M. Flyktman

. Indirect biofixation of CO 2 

### , VTT Finland, report CRE/CON 1424 produced for IEA Gre enhouse Gas R&D Programme

. Sept ember 1993.

8 USDA a gricultural land values

. March 2000

. www.usda.gov/nass/

.
9 USDA a gricultural cash rents

. July 2000

. www.usd a.gov/nass/

.

### E

- 1 

# E ESTIMATING ECONOMIC INCENTIVES REQUIRED TO ACHIEVE ADOPTION OF CONSERVATION

- TILLAGE SYSTEMS IN SELECTED REGIONS OF THE UNITE D STATES

Background
The purpose of this appendix is to provide first

- approximation regional estimates of economic incentives ($/hec tare) 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 a mounts 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 s cenarios concerning effects of reducing tillage on N fertilizer requirements and on N 2 

### O 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 N 2 

### O emissions (Chapter 20)

3

. Wheat/fallow rotations in the western Great Plains being converted from intensive

- till to no

till. This case includes vari ations in the amount of SOC sequestered and amount of N 2 

### O 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 N 2 

### O emis sions 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 S ystems 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 contacte d 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 standard ized methodology of the American Society of Agricultural Engineers (ASAE) reported by the AAEA Task Force on Commodity Costs and Returns 1



### . ASAE methodology uses estimated engineering equations along with typical machinery hours, age, size , and type to est imate 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. Cotto n 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 c osts 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 Practi ces 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 adopte d conservation tillage practices 2



### . One factor inhibiting the adoption of reduced tillage is the additional risk perceived by farmers and the perceived effects of these risks on net returns 3,4,5,6 

### . These risks include: the potential for reduced yields du

ring 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 ar e 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, farme r risk aversion behavior must be considered.
A methodological framework, referred to as the expected utility model 7 

### , provides a useful means for evaluating risk

- return tradeoffs in agricultural production settings. In the expected utilityEstimating Economic Incentives Requires to Achieve Adoption of Conservation

- Tillage Systems in Selected Regions of the United States



### E

- 3 model, the cert

ainty 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 t han 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 alte rnative 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 prof

it per unit of land area can be approximated using Equation 1 8



### . CE(NR

x 

### )=E(NR

x 

### )

- λ /2Var(NR

x 

### ) Eq. 1
Where E(NR

x 

### ) is the expected net return for tillage practi ce x, ? is the value of the Pratt

Arrow absolute risk aversion coefficient, and Var(NR x 

### ) is the variance of net returns for tillage practice x.

Freund 9 

### has shown that the linear mean

- variance objective function is consistent with the negative exponential u

tility function (assumes constant absolute risk aversion) and normally distributed profits. Thus, if CE(NR x 

### )>CE(NR

y 

### ), 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(NR x 

### )

- CE(NR

y 

### ), 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 red uced

- 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 ti llage 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 i s 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 ex tensive 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 wer e 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 th erefore , 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 till age practices in individual experiments. For example, in the c entral Corn Belt region, one study used chisel plow for theEstimating 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 bu dget 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 ret urns 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 t

o 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 crop ping 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) Intensive 9.15 2.57 0.28 434.92 IAAP

- corn No

- Till 8.76 2.19 0.25 436.67

Intensive 2.92 0.31 0.11 285.51 IAAP

- soybean No

- Till 2.81 0.30 0.11 286.84

Int ensive 9.93 1.57 0.16 434.92 Reduced 9.64 1.67 0.17 470.61 NERDF

corn 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 NERDF

soybean 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 ARC

corn 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

ARC

soybean No

- Till 3.38 0.58 0.17 277.50

Intensive 8.88 2.04 0.23 488.17 Red uced 8.42 2.25 0.27 525.86 NERDF

corn 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 ARC

corn No

- Till 9.68 1.88 0.19 453.22

Intensive 5.37 1.45 0.27 290.6 0 Cont. Sorghum RMF

sorghum No

- Till 5.24 1.32 0.25 320.95

Intensive 2.64 0.58 0.22 254.68 RMF

- 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 RMF

sorghum No

- Till 8.26 1.45 0.18 302.75

Intensive 2.56 0.76 0.30 203.31 Wheat/ Fallow SW K1

-

wheat No

- Till 2.74 0.75 0.27 249.94

Intensive 2.19 0.96 0.43 203.31 SWK1

- 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 3 04.40

Intensive 8.15 1.52 0.19 377.22 ALB

- corn No

- Till 8.67 1.27 0.14 437.04

Intensive 2.40 0.53 0.22 253.84 Corn/ Soybean ALB

soybean No

- Till 2.33 0.39 0.16 268.37

Intensive 8.94 3.03 0.34 6 94.34 Mississippi River Corridor Cotton MES

cotton No

- Till 9.07 2.65 0.29 706.32Estimating 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 aversion 10 

### . A risk aversion coefficient of .02 was selected for the analysis after revie wing the literature of risk aversion in agriculture at the per hectare level 11, 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 Inc entive levels under base yields and prices
Proposed Switch Region Site A bbv.* and Drainage Conventional

Rotation Alternative

Rotation Incentive L evel

U nder 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: Definitio n 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 wi th 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 t ype 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 y ield 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 fiel d 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 s orghum 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

- incent ive 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 R egion, 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 R egion 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 ince ntive 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 pric es) 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 t hen be estimated using the computed equations which follow the form of Equation 3 and are listed in Table E

- 5.

Equation 2: venue Base venue Base J Y P venue n

i i i 

### Re / ) 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 (% 1 0 

### venue Δ + b b
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 no

tillage yields. This causes the relative amount of revenues under conventional tillage to become

m ore “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 conve ntional 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 re venue 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 th e 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

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### E

- 9 Table E
- 3 Base yields and revenues by experimental site and rotation.
Proposed Switch Region Site A bbv .* Rotation

and Drainage Base Yields (Mg/ha) Base

Revenue ($/ha/y r)
Corn 9.15 IAAP Corn/ Soybeans: poor Soybeans 2.96 646.41

Corn 9.95 NERDF Corn/

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

Sorghum 8 RMF Sorghum/ 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

Corn 8.2 ALB Corn/ Soybeans Soybeans 2.42 556.72 Intensive Till To No

- Till

Mississippi

River Corridor MES Cont. Cotton Cotton 0.97 1,107.18

Corn 12.04 ARC Corn/ Soybeans: poor Soybeans 3.56 818. 02

Corn 9.95 NERDF Corn/

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

P roposed Switch Region Site* and Drainage Conventional

Rotation No

- Till Rotation Incentive

- L evel Equation

IAAP Poor Corn/Soybean Corn/Soybean 25.18 t +0.067 t (% Δ Rev)
r 2 =.99 NERDF well Corn/Soybean Co rn/Soybean 20.51 t +0.533 t (% Δ Rev)
r 2 =.99 NERDF well Cont. Corn Cont. Corn 66.05 t +0.681 t (% Δ Rev)
r 2 =.99 Corn Belt

ARC

poor Cont. Corn Cont. Corn 99.98 t +1.525 t (% Δ Rev)
r 2 =.99 RMF Cont.

Soybeans Cont. Soybean 6.84 t +0.00 t (% Δ Rev)
r 2 =.99 RMF Sorghum/

Soybean Sorghum/

Soybean 0.74 t

- 0.163 t (% Δ Rev)
r 2 =.99 Central Great Plains

NAF Cont. Sorghum Cont. Sorghum 36.10 t +0.052 t (% Δ Rev)
r 2 =.99 SWK1 Wheat/

Fallow Wheat/

Fallow 14.63 t

- 0.075 t (% Δ Rev)

r 2 =.99 Western Great Plains SWK1 Wheat/ Fallow W heat/Sorghum/

Fallow 15.17 t

- 0.684 t (% Δ Rev)

r 2 =.99 ALB Corn/

Soybean Corn/

Soybean 19.67 t

- 0.216 t (% Δ Rev)

r 2 =.99 Intensive Till to No

- Till

Mississippi

River Corridor MES Cont. Cotton Cont. Cotton 23.2 t +0.708 t (% Δ Rev)
r 2 =.99 ARC Poor Corn/Soybean Corn/Soybean 15.22 t +0.109 t (% Δ Rev)
r 2 =.99 NERDF Well Corn/Soybean Corn/Soybean 56.17 t +0.200 t (% Δ Rev)
r 2 =.99 ARC Poor Cont. Corn Cont. Corn 3.51 t

- 0.068 t (% Δ Rev)
r 2 =.99 Intensive Till to Reduced Till Corn

Belt

NERDF

Well Cont. Corn Cont. Corn 101.26 t +0.8 97 t (% Δ Rev)

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

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### 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 Ser vice, 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 conservatio n 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. “Stoch astic Dominance: The state of the art in agricultural economics

. ” Proceeding of a Seminar Sponsored by Southern Regional Project S

- 180, Tampa, F lorida , March 23

- 26, 1986. Dept of Ag Economics, Washington State Univ. Pullman, Washington.

12 Williams, J effery 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

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### E

- 12 13 Sunding, D., and D. Zilberman. 2000. The Agricultura l Innovation P rocess: 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 Publ ications.

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 P resented at the AAEA 2001 A nnual 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

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- 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: 1997-2001Estimating Economic Incentives Requires to Achieve Adoption of Conservation

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- 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 NOTILLEstimating Economic Incentives Requires to Achieve Adoption of Conservation

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- 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: 1981-1994Estimating 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 a nd SERDF sites. Both NERDF and SERDF experiments are carried out in small test plots in Iowa. 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 “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 a s 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. A lthough 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. Conti nuous 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 “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 a s the representative for well

- drained soils

. Continuous Corn Poorl y Drained Soil There are three continuous corn data sets from poorly drained soils, SERDF, PPAC and ARC sites. As stated above , an e astern 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/fal low 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 informati on. 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

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- 17 

## Attachment E
- 3 of Appendix E: 

## Yield Comparisons

50 60 70 80 90 100 110 120 130 140 150 160 170 180 Y i e l d 0 5 10 15 20 25Frequency 

### C o r n B e l t : C o r n / S o y b e a n R o t a t i o n I o w a A r m y A m m u n i t i o n P l a n t ( I A A P ) B u r l i n g t o n , I A C o r n Y i e l d , p o o r l y d r a i n e d

I n t e n s i v e m e a n = 1 3 6

. 7 N o - T i l l m e a n = 1 3 1

. 0Estimating 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 Y i e l d 0 10 20 30 40frequency 

### C o r n B e l t : C o r n / S o y b e a n R o t a t i o n I o w a A r m y A m m u n i t i o n P l a n t ( I A A P ) B u r l i n g t o n , I A S o y b e a n Y i e l d , p o o r l y d r a i n e d

I n t e n s i v e m e a n = 4 3

. 5 N o - T i l l m e a n = 4 2

. 0Estimating 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 Y i e l d 0 4 8 12 16frequency 

### C o r n B e l t : C o n t i n u o u s C o r n

N o r t h e a s t R e s e a r c h a n d D e m o F a r m ( N E R D F ) N a s h u a , I A C o r n Y i e l d , w e l l d r a i n e d

I n t e n s i v e m e a n = 1 3 2

. 7 N o - T i l l m e a n = 1 2 2

. 2Estimating 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 Y i e l d 0 10 20 30frequency 

### C o r n B e l t : C o r n / S o y b e a n R o t a t i o n

N o r t h e a s t R e s e a r c h a n d D e m o F a r m ( N E R D F ) N a s h u a , I A S o y b e a n Y i e l d , w e l l d r a i n e d

I n t e n s i v e m e a n = 4 5

. 3 N o - T i l l m e a n = 4 4

. 3Estimating 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 Y i e l d 0 4 8 12 16frequency 

### C o r n B e l t : C o n t i n u o u s C o r n

N o r t h e a s t R e s e a r c h a n d D e m o F a r m ( N E R D F ) N a s h u a , I A C o r n Y i e l d , w e l l d r a i n e d

I n t e n s i v e m e a n = 1 3 2

. 7 N o - T i l l m e a n = 1 2 2

. 2Estimating 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 Y i e l d 0 5 10 15 20 25frequency 

### C o r n B e l t : C o n t i n u o u s C o r n P u r d u e A g r o n o m y R e s e a r c h C e n t e r ( A R C ) W

. L a f a y e t t e , I N C o r n Y i e l d , p o o r l y d r a i n e d

I n t e n s i v e m e a n = 1 6 7

. 5 N o - T i l l m e a n = 1 4 4

. 7Estimating 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 Y i e l d 0 5 10 15 20 25frequency 

### C o r n B e l t : C o r n / S o y b e a n R o t a t i o n P u r d u e A g r o n o m y R e s e a r c h C e n t e r ( A R C ) W

. L a f a y e t t e , I N C o r n Y i e l d , p o o r l y d r a i n e d

I n t e n s i v e m e a n = 1 7 8

. 8 R e d u c e d m e a n = 1 8 0

. 4Estimating 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 Y i e l d 0 5 10 15 20 25frequency 

### C o r n B e l t : C o r n / S o y b e a n R o t a t i o n P u r d u e A g r o n o m y R e s e a r c h C e n t e r ( A R C ) W

. L a f a y e t t e , I N S o y b e a n Y i e l d , p o o r l y d r a i n e d

I n t e n s i v e m e a n = 5 2

. 9 R e d u c e d m e a n = 5 0

. 7Estimating 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 Y i e l d 0 4 8 12 16 20frequency 

### C o r n B e l t : C o r n / S o y b e a n R o t a t i o n

N o r t h e a s t R e s e a r c h a n d D e m o F a r m ( N E R D F ) N a s h u a , I A C o r n Y i e l d , w e l l d r a i n e d

I n t e n s i v e m e a n = 1 4 8

. 4 R e d u c e d m e a n = 1 4 4

. 1Estimating 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 Y i e l d 0 10 20 30frequency 

### C o r n B e l t : C o r n / S o y b e a n R o t a t i o n

N o r t h e a s t R e s e a r c h a n d D e m o F a r m ( N E R D F ) N a s h u a , I A S o y b e a n Y i e l d , w

e l l d r a i n e d I n t e n s i v e m e a n = 4 5

. 3 R e d u c e d m e a n = 4 3

. 6Estimating 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 Y i e l d 0 5 10 15 20 25frequency 

### C o r n B e l t : C o n t i n u o u s C o r n

P u r d u e A g r o n o m y R e s e a r c h C e n t e r ( A R C ) W

. L a f a y e t t e , I N C o r n Y i e l d , p o o r l y d r a i n e d

I n t e n s i v e m e a n = 1 6 8

. 8 R e d u c e d M e a n = 1 6 5

. 8Estimating 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 Y i e l d 0 4 8 12frequency 

### C o r n B e l t : C o n t i n u o u s C o r n

N o r t h e a s t R e s e a r c h a n d D e m o F a r m ( N E R D F ) N a s h u a , I A C o r n Y i e l d , w e l l d r a i n e d

I n t e n s i v e = 1 3 2

. 7 R e d u c e d m e a n = 1 2 5

. 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 swit ching 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 res ults 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 f or 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 = -10.5x 2



### + 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 tillageEstimating Sta te
- 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/haE

Figure F
- 2 Linea r 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/haE

Figure F
- 3 Linear relationship between E and ?SOCs for the Iowa data from Table F

- 2Estimating 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 ? SOCEstimating Sta te
- 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 curvesEstimating 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 soilsEstimating Sta te
- 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 ? SOCEstimating 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 curvesEstimating Sta te
- 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 soilsEstimating 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 pr actice 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, C olorado , 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, C olorado , USA.

3 Bock, B. R., F. J. Sikora, and G. W. Hergert. 1991. Approach for estimatin g yield response to nitrogen based on nitrogen

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