New Cost Estimates for 

Carbon Sequestration 

Through Afforestation 

in the United States

Anne Sofie Elberg Nielsen, Andrew J. Plantinga, 

and Ralph J. Alig

Pacific Northwest 

Research Station

General Technical Report 

PNW-GTR-888

March 2014

DEPAR TMENT OF AGRICULT URE

United States Department of Agriculture

Forest Service
Authors

Anne Sofie Elburg Nielsen is a Ph.D. student, Department of Food and Resource 

Economics and Centre for Macroecology Evolution and Climate, University of 

Copenhagen, Rolighedsvej 23, DK-1958 Frederiksberg C, Denmark; Andrew J. 

Plantinga is a professor, Bren School of Environmental Science and Management, 

University of California, Santa Barbara, CA 93106; and Ralph J. Alig is an 

(emeritus) economist, U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331.

Cover photograph by Frank Vanni.

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Abstract

Nielsen, Anne Sofie Elburg; Plantinga, Andrew J.; Alig, Ralph J. 2014. New 

cost estimates for carbon sequestration through afforestation in the United States. 

Gen. Tech. Rep. PNW-GTR-888. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 35 p.

This report provides new cost estimates for carbon sequestration through afforestation in the United States. We extend existing studies of carbon sequestration 

costs in several important ways, while ensuring the transparency of our approach. 

We clearly identify all components of our cost estimates so that other researchers 

can reconstruct our results as well as use our data for other purposes. Our cost 

estimates have five distinguishing features: (1) we estimate costs for each county in 

the contiguous United States; (2) we include afforestation of rangeland, in addition 

to cropland and pasture; (3) our opportunity cost estimates account for capitalized 

returns to future development (including associated option values) in addition to 

returns to agricultural production; (4) we develop a new set of forest establishment 

costs for each county; and (5) we incorporate data on Holdridge life zones to limit 

afforestation in locations where temperature and moisture availability prohibit 

forest growth. We find that at a carbon price of $50/ton, approximately 200 million 

tons of carbon would be sequestered annually through afforestation. At a price of 

$100/ton, an additional 100 million tons of carbon would be sequestered each year. 

Our estimates closely match those in earlier econometric studies for relatively low 

carbon prices, but diverge at higher carbon prices. Accounting for climatic constraints on forest expansion has important effects on cost estimates.

Keywords: Carbon sequestration, afforestation, cost estimates.

To access county-level data on land prices, tree establishment costs, carbon 

uptake rates, and eligible land for conversion to forest, follow this link: 

http://www.fs.fed.us/pnw/pubs/pnw_gtr888/county-level-data_nielsen2013.xlsx
Contents

1 Introduction

3 Data Development

13 Cost Analysis and Results

18 Comparison to Other Studies

21 Limitations of This Study 

22 Conclusions

24 References

28 Appendix
1

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

Introduction

The first studies on the cost of sequestering carbon in forests appeared in the late 

1980s (Dudek and LeBlanc 1990, Marland 1988, Sedjo 1989) and provided point 

estimates of the average cost of forest carbon sequestration. Moulton and Richards 

(1990) provided the first marginal cost estimates for the United States. Marginal 

costs are useful because they can be combined with cost estimates for other carbon 

mitigation and abatement approaches to identify the efficient portfolio of strategies. 

To estimate marginal costs, Moulton and Richards first estimated average costs of 

carbon sequestration for 10 U.S. regions and seven treatment types (afforestation on 

wet and dry cropland, afforestation on wet and dry pasture, and three forest management treatments). These estimates accounted for opportunity costs of the land, 

upfront treatment costs, and the total amount of carbon sequestered. Moulton and 

Richards constructed a marginal cost curve by ordering these costs from lowest to 

highest and then plotting them against cumulative carbon sequestration.

Following Moulton and Richards, economists have provided a number of refinements to the methodology for estimating marginal costs (Dempsey et al. 2010). 

Adams et al. (1993) recognized that a national afforestation policy would raise the 

marginal costs of carbon sequestration by restricting the supply of agricultural 

commodities, thus increasing their prices and the opportunity cost of conversion to 

forest. Similarly, more land in forest would increase the supply of wood products, 

diminishing these prices and the willingness of landowners to afforest. By combining models of the timber and agricultural sectors, these authors demonstrated that 

price feedbacks raise the marginal costs of carbon sequestration, particularly as the 

total amount of carbon sequestered increases.1

 Other studies that account for endogenous price feedbacks from forest carbon sequestration policies include Richards et 

al. (1993), Alig et al. (1997), Adams et al. (1999), and Lubowski et al. (2006).

A second refinement is to measure opportunity costs of land using econometric 

analysis, rather than bottom-up engineering methods, as in Moulton and Richards, 

or sectoral optimization models, as in the Forest and Agricultural Sector Optimization Model (FASOM) studies. The econometric approach involves analyzing 

historical data on the actual decisions by landowners facing returns to alternative 

uses. Once the relationship between land use choices and net returns is identified, 

a policy simulation is conducted to estimate the response by landowners to incentives for afforestation or avoided deforestation. The econometric approach has the 

1 The model in Adams et al. (1993) evolved into the Forest and Agricultural Sector 

Optimization Model (FASOM), which integrates the forest and agricultural sectors through 

competition for land (Adams et al. 1996). 
2

GENERAL TECHNICAL REPORT PNW-GTR-888

potential to account for factors that affect land use decisions in practice but that are 

difficult to measure explicitly. These include option value related to holding land in 

its current use, as well as private nonmarket benefits (e.g., recreation) that landowners may derive from land in particular uses. Typically, marginal cost estimates from 

econometric analyses are higher than those produced with bottom-up engineering 

or optimization methods (Lubowski et al. 2006, Plantinga et al. 1999, Stavins 1999). 

There have also been a few studies that estimate opportunity costs using a stated 

preference approach (e.g., van Kooten et al. 2002).

Three other innovations in the literature since Moulton and Richards deserve 

mention. The first relates to carbon accounting. Moulton and Richards compute 

the average annual increment in carbon over a 40-year project horizon. As Stavins 

(1999) points out, this ignores the time profile of carbon flows into and out of the 

forest. He proposed, as an alternative, discounting carbon flows and then annualizing the present value expression, an approach that has become standard practice 

in carbon sequestration studies. The second innovation has been to expand the 

scope of studies to other countries besides the United States. Although the greatest 

number of estimates has been produced for the United States, the review by Richards and Stokes (2004) includes global cost studies and estimates for a number of 

non-U.S. countries. Finally, recent forest carbon sequestration studies by Latta et al. 

(2011), Mason and Plantinga (2011), and Busch et al. (2012) have given more careful 

consideration to policy design and implementation and the effects this has on cost 

estimates. 

This report provides new cost estimates for carbon sequestration through 

afforestation in the United States. The methodological advances since Moulton 

and Richards have helped to provide a more realistic assessment of the costs of a 

forest carbon sequestration policy. In particular, accounting for endogenous price 

effects and unobserved landowner behavior have revised upward the cost estimates 

reported in early studies. Unfortunately, the new methods have come at the cost 

of transparency. In Moulton and Richards, each point on the marginal cost curve 

can be traced to the underlying estimates of opportunity costs, converted land, 

treatment costs, and carbon uptake. In the newer studies, these variables are more 

complicated. For example, in a model with endogenous prices, the cost of carbon 

sequestration at each location is time-varying and dependent on many variables 

measured at other locations. Consequently, it can be difficult to identify and understand the factors that lie behind a cost estimate for a particular location.

Our objective is to extend existing carbon sequestration cost estimates in several important ways, while retaining the transparency of the original Moulton and 

Our objective is to 

extend existing carbon 

sequestration cost 

estimates in several 

important ways, 

while retaining the 

transparency of the 

original Moulton and 

Richards study.
3

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

Richards study. Our results clearly identify all components of our cost estimates, 

and we make available all of the data so that other researchers can reconstruct our 

results as well as use the data for other purposes. Our costs estimates have five 

distinguishing features. First, we estimate costs for each county in the contiguous 

United States, rather than for the aggregate regions used in Moulton and Richards 

and other studies. Second, our estimates include afforestation of rangeland, in 

addition to cropland and pasture.2 Third, our opportunity cost estimates account for 

capitalized returns to future development in addition to returns to agricultural production. These estimates account for option value associated with holding land in 

agriculture while new information about the profitability of development becomes 

available. Fourth, we develop a new set of forest establishment costs for each county 

using data from the Conservation Reserve Program (CRP). Fifth, we incorporate 

data on Holdridge life zones to limit afforestation in locations where temperature 

and moisture availability prohibit forest growth. To keep the analysis simple, we 

do not account for endogenous prices and we assume that our cost estimates reflect 

all of the relevant factors affecting land use choices. However, we use results from 

other studies to suggest how our cost estimates would change were we to include 

these refinements. 

In the “Data Development” section, we discuss the development of the data 

used in the study, and in the “Cost Analysis and Results” section we present results. 

Data Development

We need four key pieces of information to construct estimates of the cost of sequestering carbon through afforestation. First, we measure average land prices in each 

county by use as an estimate of the opportunity cost of converting agricultural land 

to forest. Second, we estimate the cost of establishing forests in each county. Third, 

we use information on Holdridge life zones to identify the area of private land in 

each county that can feasibly be converted to forest. Lastly, we estimate the average carbon uptake by new forests in each county. These data are combined, in this 

section, to produce cost estimates. 

Land prices—

We measure the opportunity costs of the land by constructing estimates of land 

prices for three uses (cropland, pasture, and rangeland) and each county. We start 

with the 1997 county average estimates of farmland value reported in the Census of 

2 Moulton and Richards also evaluated forest management approaches, which we do 

not consider.
4

GENERAL TECHNICAL REPORT PNW-GTR-888

Agriculture. These are self-reported estimates of the per-acre market value of farmland, where farmland includes all land that is part of a farm. Thus, it can include 

cropland, pasture, rangeland, and forest.3

 We denote this value FARMVAL. In an 

earlier study using these data, Plantinga et al. (2002) used econometric methods to 

decompose FARMVAL into components measuring near-term discounted returns 

to agriculture and future discounted returns to urban development. We denote 

the average capitalized development value per acre as DEVVAL. As explained in 

Plantinga et al., this estimate accounts for the option value associated with holding 

land in agricultural use in order to obtain forthcoming information about the profitability of development.4

 We assume that this development value is reflected in the 

returns to all private cropland, pasture, and rangeland in a county. This assumption 

is appropriate insofar as the average acre of a Census classified farm is representative of all private rural lands. The component of the per-acre farmland value owing 

to agricultural production is AGVAL = FARMVAL – DEVVAL.

Suppose that the average farmland parcel in a county will be used for agricultural production for the next T years, after which time it will be converted to 

developed use. Assume that annual per-acre farm net revenues (FARMNR) are 

constant through time and that the discount rate is r. Then, we can write the agricultural value as:

AGVAL=FARMNR × (1)

We compute FARMNR using the 1997 Census of Agriculture statistics. For 

each county, farm net revenues equals the market value of agricultural products 

sold minus total farm production expenses plus total government payments. We 

divide this figure by the area of land in farms to obtain an annual per-acre farm net 

revenue estimate. With estimates of AGVAL and FARMNR, we can estimate the 

discounting term in (1) for each county as:

= AGVAL / FARMNR (2)

This term implicitly accounts for the unobserved average time to development 

in each county, which we assume is constant for each use. 

T

T

(1 r) 1

r(1 r)

+ −

×

+

AGVAL = FARMNR

T

T

(1 r) 1

r(1 r)

+ −

+

= AGVAL / FARMNR

3 These estimates also include the value of farmland buildings (homesteads, barns, etc.). 

Unfortunately, there is no way to isolate only the land component of farmland value.

4 Option values could also be associated with other land use conversions (e.g., pasture to 

cropland). We account only for the option value arising from agriculture-to-development 

conversions, though we expect this to be the most important one. See, also, Schatzki 

(2003) for an examination of option values in the context of cropland to forest conversions.
5

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

The final step is to estimate the land prices by use. Lubowski (2002) constructed county-level estimates of annual per-acre net revenues for each major land 

use. We use the 1997 estimates for crops, pasture, and range, which we denote 

CROPNR, PASTURENR, and RANGENR. Assuming these net revenues are 

constant through time, we can write land prices by use as:

CROPPRICE = CROPNR × (AGVAL / FARMNR) + DEVVAL

PASTUREPRICE = PASTURENR × (AGVAL / FARMNR) + DEVVAL (3)

RANGEPRICE = RANGENR × (AGVAL / FARMNR) + DEVVAL

If, on average, farm net revenues were unusually low, or even negative, in 1997, 

we can obtain either implausible large or negative price estimates. To address the 

first problem, we use FARMVAL as an upper bound on each of the price estimates. 

For the second problem, we replace the AGVAL/FARMNR term with 20. If r = 5 

percent, then the left-hand side of (2) approaches 20 as T goes to infinity. This new 

price estimate is also bounded from above by FARMVAL. Maps showing per-acre 

prices for each use and county are found in the appendix (figs. 11 through 13).

For scenarios in which newly established forests are periodically harvested, 

we subtract from each land price an estimate of the present discounted value of net 

revenues from timber harvesting. Using Lubowski’s (2002) estimates of per-acre 

annualized forest net returns for each county, FORESTNR, we compute the present 

value of harvesting revenues as FORESTNR/0.05. Maps displaying the three land 

prices net of harvesting revenues are presented in the appendix (figs. 14 through 16). 

Establishment costs—

We measure the costs of establishing forest on crop, pasture, or range land using 

estimates from the CRP during the period 1986–1993. During these first years 

of the CRP, the main objective of the program was to enroll large areas of erodible cropland, whereas in later years, the program targeted parcels providing high 

levels of environmental benefits. The data from earlier years is, thus, more suited 

to measuring average establishment costs for all lands within a county. The CRP 

is a voluntary cropland retirement program under which land is converted to grass, 

trees, or other qualifying land covers. In exchange, the landowner receives an 

annual payment as well as up to 50 percent of the cost of establishing the alternative 

cover. We use the county average cost shares for trees, multiplied by two, to estimate forest establishment costs for each county. This implicitly assumes that costs 

of establishing forests on cropland are the same as for pasture and range. Because 

rangelands are typically found in arid regions, we may underestimate the costs of 

We measure the costs 

of establishing forest 

on crop, pasture, 

or range land using 

estimates from the 

CRP during the period 

1986–1993.
6

GENERAL TECHNICAL REPORT PNW-GTR-888

forest establishment on these lands. However, as discussed below, this problem 

is mitigated to some degree because we exclude lands that cannot support forests 

owing to climatic conditions. Finally, we assume that the cost estimates correspond 

to the costs of planting the existing mix of tree species found in each county. We 

adopt this assumption to allow for forests established through natural regeneration 

(e.g., hardwoods) as well as forests that are actively planted (e.g., softwood plantations). The CRP data provide a better measure of the costs of establishing plantations, and so we are likely to overstate forest establishment costs. 

Because the CRP focused on erodible cropland and was voluntary, the available 

observations of forest establishment costs are dependent on the geographic distribution of factors such as land quality and weather. As shown in figure 1, observations 

of forest establishment costs are available for most counties in the Eastern United 

States, but unavailable for many of the western counties. To develop estimates 

for the missing counties, we regress observed CRP establishment costs on physiographic variables and use the estimated relationship to predict costs in the rest 

Figure 1—Original data from the Conservation Reserve Program on costs of tree establishment ($/acre).
7

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

of the counties. Because of potential sample selection in the observed data arising 

from the nonrandom distribution of factors that affect the potential for afforestation 

(e.g., land quality, climatic factors), a two-stage Heckman sample selection model 

was used.

The dependent variable in the cost model is the average per-acre forest establishment cost in each county i (CRPCOSTi

): 

CRPCOSTi = (4)

where Xi

 is a vector of physiographic variables for county i; Di is a dummy variable 

that takes the value 1 if a county is east of the 100th meridian, and 0 otherwise; Gi

is a dummy for values of forest establishment costs that exceed $500; is ˆ φ ' CRPCOST =i α i i i i i i +β X + δD + D G + ρλ +the ε

estimated inverse Mills ratio from the first-stage estimation; and ˆ φ ' CRPCOST =i α i i i i i i +β X + δD + D G + ρλ + ε is a mean-zero 

random error term. 

The physiographic variables include the share of total cropland in the county in 

Holdridge forest zones and the share of total CRP-eligible cropland in the county 

in Land Capability Classes (LCCs) IIe-Ve. The cropland share in Holdridge zones 

was included to account for the potential for cropland in the county to be afforested. 

If a large share of cropland is located in Holdridge forest zones, it is expected that 

conversion costs would be relatively lower since more land is suitable for growing 

trees. The CRP eligibility was generally limited to cropland in LCC VI-VIII and 

erodable cropland in classes II-V, where a higher LCC class indicates lower soil productivity for agriculture. We expect that forest establishment costs would be lower 

on higher quality land, and so a greater share of eligible cropland in classes IIe-Ve 

should be negatively related to costs.5

To account for location-specific factors in each county, such as climate and 

types of trees that grow there, we include variables for the longitude and latitude of 

the county center as well as the squares of these variables. Further, a dummy variable for counties east of the 100th meridian is included to account for the east-west 

shift in precipitation that occurs at this longitude. We expect forest establishment 

costs to be lower in Eastern counties owing to greater rainfall. Finally, a small number of counties in the eastern United States have unusually high average costs. In 

these counties, it is likely that few CRP tree planting contracts were established. To 

ˆ φ ' CRPCOST =i α i i i i i i +β X + δD + D G + ρλ + ε

5 We have estimates of the area of cropland by LCC in each county. These were obtained 

by overlaying an LCC map on the 2001 National Land Cover Database map. We obtained 

separate estimates of the breakdown of each LCC into subclasses (erodable, wet, etc.) 

for all land in the county. For LCC II-V, we multiplied the share of land in the erodable 

subclass by the cropland acres in the corresponding LCC to obtain an estimate of erodable 

cropland by LCC.
8

GENERAL TECHNICAL REPORT PNW-GTR-888

limit the effects of outliers on the regression results, we include a dummy variable 

for costs in excess of $500 per acre interacted with the Eastern dummy.

We only observe forest establishment costs for counties with a positive number 

of acres planted to trees under the CRP. The dependent variable in our selection 

equation is a binary variable that takes the value 1 if a county has positive CRP tree 

enrollment and 0 otherwise. The independent variables are physiographic variables 

that explain whether cropland in the county is likely to support trees. We include a 

dummy variable indicating if any of the cropland in the county is in the Holdridge 

forest zone. This variable is expected to have a positive effect on the likelihood of 

CRP tree enrollment.6 Second, we include a measure of the acres of CRP-eligible 

cropland in a county. Not all eligible farmers participated in the CRP, but the 

probability that some did is likely to increase with the number of eligible acres. 

To control for climate, we include variables for longitude and latitude, as well as 

a measure of the annual precipitation. Greater rainfall is expected to increase the 

likelihood of tree planting under the CRP.

Estimation results are presented in table 1. The estimates of the coefficients in 

the selection equation are consistent with expectations. The presence of cropland 

in the Holdridge forest zone, acres of eligible cropland, and annual precipitation 

all increase the likelihood of CRP tree planting. The coefficients on longitude and 

latitude are both positive, indicating that, all else equal, the likelihood of CRP tree 

planting increases as one moves north and east. All of the coefficient estimates are 

significantly different from zero at the 1-percent level.

The results for the cost equation are mixed. The coefficient on the cropland 

share in Holdridge forest is positive, contrary to expectations, but significantly 

different from zero at only the 10-percent level. The share of CRP-eligible cropland 

in lower LCCs does not have a significant effect on forest establishment costs. The 

coefficient on the east dummy variable is significantly different from zero at the 

1-percent level and indicates that costs are almost $200/acre lower in the east. All 

else equal, costs fall as one moves south and rise as one moves west, as reflected in 

figure 1. The squared longitude and latitude variables are also significantly different 

from zero at the 1-percent level. Finally, the coefficient on the inverse Mills ratio is 

significantly different from zero, indicating a nonrandom distribution of observed 

CRP costs.

6 This variable is important for the identification of the model. The absence of any cropland 

in the Holdridge forest zone should be strongly correlated with the binary dependent variable for CRP tree enrollment, but would not explain differences across counties in observed 

establishment costs in counties with CRP tree enrollment. 
9

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

Table 1—Estimation results for the cost and selection equations

Cost equation Selection equation

Cropland share in Holdridge forest 0.283+ Cropland in Holdridge forest 0.547**

(0.156) (0.104)

Cropland share in LCCa IIe-Ve -8.647 Precipitation 0.027**

(25.919) (0.003)

Longitude -19.279* Acres of eligible cropland 0.290**

(8.243) (0.000)

Longitude squared -0.095* Longitude 0.019**

(0.046) (0.003)

Latitude 149.796** Latitude 0.046**

(16.874) (0.006)

Latitude squared -1.843** Constant -1.497**

(0.219) (0.430)

East -190.041**

(31.530) 

East outlier 828.354**

(45.584) 

Constant -3699.582**

(490.590) 

Inverse Mills ratio 109.515**

(34.863) 

Note: + p < 0.10, * p < 0.05, ** p < 0.01. a LCC = land capability class.

Using the estimated cost equation, we predict forest establishment costs for the 

counties with missing observations (fig. 2). In figure 2, we also plot the observed 

average costs for those counties with CRP tree planting. The results show considerably higher costs in western counties, as one would expect given that the climate is 

typically drier and, thus, less hospitable to trees. Note that these estimates account 

only for stand establishment costs, and not any ongoing costs of managing forests.

Holdridge life zones—

The Holdridge life zone system is an ecosystem classification scheme for land 

(Holdridge 1967). Four variables—precipitation, biotemperature, potential evapotranspiration, and elevation—are used to classify each land area as a distinct ecological unit. Lugo et al. (1999) map these variables for the contiguous United States 

and identify 38 life zones. For example, most of the contiguous United States east of 

the 100th meridian is comprised of three life zones: warm temperature moist forest, 

cool temperature moist forest, and cool temperature wet forest. Much more heterogeneity is found west of the 100th meridian. In total, 19 of the life zones correspond 

to a type of forest, which we refer to collectively as “Holdridge forest.” That a given 

location is classified as Holdridge forest does not necessarily mean that forests are 

The results show 

considerably higher 

costs in Western 

counties, as one would 

expect given that the 

climate is typically 

drier and, thus, less 

hospitable to trees.
10

GENERAL TECHNICAL REPORT PNW-GTR-888

found there; rather, the classification indicates that the climate at that location could 

support forests. This point is well demonstrated by the Corn Belt. This region is 

classified as Holdridge forest, though the majority of the land is in crops and other 

nonforest uses.

We use the Holdridge life zones to identify areas in the contiguous United 

States where there is potential for forest to be grown. Specifically, we overlay maps 

of Holdridge forest zones, land cover, and ownership to identify, for each county, 

the area of privately owned crop, pasture, and range lands that can be converted to 

forest. Holdridge forest zones are identified using the map described in Lugo et al. 

(1999). The 2001 National Land Cover Database (NLCD) is used to identify crop, 

pasture, and range lands. Crop and pasture are defined by NLCD categories 82 

(Cultivated Crops) and 81 (Pasture/Hay), respectively. West of the 100th meridian, 

we define range by categories 71 (Grassland/Herbaceous) and 52 (Shrub/Scrub) and, 

east of 100th meridian, only by category 71. The Shrub/Scrub category includes true 

shrubs as well as trees in an early successional stage. In the east, there are large 

Figure 2—Observed and predicted costs of tree establishment ($/acre).
11

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

areas of land in the Shrub/Scrub category owing to the presence of young trees.7

We treat these lands as already forested. Lands that are enrolled in a nonforest use 

under the CRP are likely to be found in categories 71 or 81. Finally, we use an ownership map from Conservation Biology Institute (2010) to identify private lands.

In the appendix, we present maps showing the share of each county in Holdridge forest zones by use (figs. 17 through 19). 

Carbon—

Forest carbon is stored in biomass (e.g., live trees and understory vegetation), in 

standing dead trees, in fallen dead wood, and in floor litter and soils (Smith et al. 

2006). At the time a forest stand is established, most of the carbon will be found in 

the soils. However, as trees and other plants convert CO2 to carbon through photosynthesis, in most forests the carbon stored in biomass will eventually exceed the 

carbon in soils. We use the tables in Smith et al. (2006) to measure carbon flows 

for the forest types found in each region.8

 A planning horizon of 85 years is 

assumed in each case. This is long enough so that carbon flows beyond this point 

have little effect on the present discounted value of carbon flows assuming a 5 

percent annual rate. For each forest type, we compute the present value of carbon 

flows for the cases with no timber harvesting and periodic harvesting at rotation 

length specified in Smith et al. (2006). Following harvest, some carbon in the stand 

will be rapidly converted back to CO2 through decomposition or burning for energy 

production.9

 Some portion of the carbon in the merchantable portion of trees will be 

fixed for decades in wood products (e.g., lumber, plywood) and, following disposal, 

in landfills. 

The carbon yield for a representative forest stand in each county is constructed 

as a weighted average of the forest type-specific discounted flows. The weights 

reflect the composition of tree species currently found in each county. Species 

weights were obtained from U.S. Forest Service, Forest Inventory and Analysis 

(FIA) studies conducted during the 1990s (Lubowski 2002). When a species was 

not clearly represented by a forest type in Smith et al. (2006), we paired the species 

7 Our definition of rangeland is close to that used in the National Resources Inventory 

(NRI): “Rangeland is a land cover/use category on which the climax or potential plant 

cover is composed principally of native grasses, grasslike plants, forbs, or shrubs suitable 

for grazing and browsing, and introduced forage species that are managed like rangeland.” 

By the NRI definition, there is very little rangeland in the Eastern United States. In 

contrast, if we define rangeland to include Shrub/Scrub in the Eastern United States, the 

NLCD implies a large amount of rangeland. 

8 Forest type refers to the collection of dominant tree species in a forest.

9 The substitution of biomass for fossil fuels does not generate a one-to-one emissions 

offset, but rather delays the release of carbon from fossil fuels. Accounting for the induced 

change in the time path of fossil fuel use and associated emissions is beyond the scope of 

our study.
12

GENERAL TECHNICAL REPORT PNW-GTR-888

with a similar forest type within the region or the forest type from a neighboring 

region. The final result is the present value of discounted carbon flows (metric tons 

per acre) for a representative forest stand in each county. Separate values are produced for forests with and without harvesting, though we do not distinguish among 

initial land uses owing to lack of data.

Results are shown in figures 3 and 4. Without harvesting, carbon uptake is 

greatest in the Lake states, Northeast and Pacific coast regions. With harvesting, 

carbon uptake in the South increases, while it drops in parts of the North. Yields 

decline more quickly over time in the South compared to the North, which explains 

the lower carbon uptake without harvesting. With periodic harvesting, carbon 

uptake increases in the South as average yields increase and carbon is accumulated 

in wood products pools. In the North, the loss of carbon at the first harvest lowers 

the present value of carbon flows.

Figure 3—Carbon uptake without periodic harvesting (present value metric tons/acre).
13

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

Cost Analysis and Results

Using the above information, we compute the cost per ton of carbon for each land 

use and county. The opportunity cost of the land is the sum of the establishment 

cost and the land price corresponding to the initial use. If forests are periodically 

harvested, we use the land price net of harvesting revenues as these revenues 

decrease the opportunity costs of afforestation. The opportunity cost is then divided 

by the present discounted value of carbon corresponding to the respective noharvesting and harvesting cases. Implicit in this calculation is the assumption that 

land will be afforested today and remain in forest thereafter.

Figure 4—Carbon uptake with periodic harvesting (present value metric tons/acre).
14

GENERAL TECHNICAL REPORT PNW-GTR-888

We summarize the cost results by computing the average cost per ton of carbon in 

each county, using the land use shares as weights. Figures 5 and 6 show the cases 

without and with harvesting (maps showing the cost per ton of carbon separately 

for each use are presented in appendix figures 20 through 22). Allowing harvesting affects both the carbon yields and opportunity costs of afforestation. As noted 

above, the effect on carbon uptake is ambiguous, varying with regional climatic 

conditions, forest species, and yields. In contrast, harvesting raises the value of 

land in forests, thereby lowering the opportunity cost of afforestation. The impact 

of harvesting on the national range of average carbon sequestration costs is minor: 

average costs are between $1.50 and $581 per metric ton with no harvesting and 

between $0.03 and $580 per metric ton with harvesting. However, the overall 

pattern revealed in figures 5 and 6 indicates that harvesting reduces per-ton costs in 

most counties.

Figure 5—Average carbon sequestration costs without periodic harvesting ($ per metric ton).
15

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

A national-level marginal cost curve for carbon sequestration can be constructed using these results. As in Lubowski et al. (2006), we order the cost-per-ton 

estimates and then plot them against the annualized cumulative carbon sequestered. 

The results indicate the total carbon uptake per year that would result for given 

per-ton carbon payments (fig. 7). With periodic harvesting, a carbon price of $50 

per ton generates 200 million tons of carbon annually, corresponding to approximately 13 percent of the annual emissions of the United States.10 By comparison, 

with no harvesting, a $50-per-ton price yields only 127 million tons of carbon. At a 

$100-per-ton price the sequestration level is 305 million tons of carbon with periodic harvesting, while further increases result in smaller incremental changes in 

the sequestration level (e.g., 351 tons at $150 per ton). After a price of $200 per ton, 

little remaining land is available for afforestation, and as a consequence, further 

price increases have little effect on total carbon uptake. 

Figure 6—Average carbon sequestration costs with periodic harvesting ($ per metric ton).

10 The estimated U.S. net greenhouse gas emissions in 2009 were 1,532 million tons of 

carbon equivalent (U.S. Environmental Protection Agency 2011).

With periodic 

harvesting, a carbon 

price of $50 per 

ton generates 200 

million tons of 

carbon annually, 

corresponding to 

approximately 13 

percent of the annual 

emissions of the 

United States.
16

GENERAL TECHNICAL REPORT PNW-GTR-888

For the rest of this section, we focus on results with periodic harvesting because 

this involves lower costs. Figure 8 presents marginal cost curves individually by 

land use. Range provides the cheapest carbon sequestration, at least until the stock 

of available land is used up, followed by cropland and finally pasture. The higher 

marginal costs for pasture are related, in part, to the fact that there is less land 

nationwide in pasture, which means that the marginal cost curve for pasture turns 

up sooner than those for the other uses. 

More details on these results are provided in table 2. The $50 carbon price 

results in the afforestation of 246 million acres with about one-half of this on 

rangeland. Increasing the carbon price to $100, the low cost rangeland is largely 

exhausted, and cropland contributes about 40 percent of the total 392 million 

afforested acres. The change from $50 per ton to $100 per ton doubles the area 

of afforested pasture. At a carbon price of $150 per ton, the largest contribution 

comes from cropland. Table 2 also reports the total carbon sequestered and the total 

opportunity cost of the policy for each land use and carbon price. The opportunity 

Figure 7—Marginal costs of carbon sequestration in forests in the United States ($/metric ton/year).

0 100 200 300 400

0 50 100 150 200 250

$/metric tons per year

Million tons carbon per year

Cost without harvesting Cost with harvesting
17

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

Table 2–Summary of carbon sequestration cost results

Carbon prices (per ton)

$50 $100 $150

Area afforested: Million acres

 Crops 79.2 158.0 206.6

 Pasture 48.2 96.0 113.2

 Range 118.4 138.4 141.7

 Total 245.8 392.4 461.5

Carbon sequestration: Million tons per year

 Crops 63.8 119.7 150.1

 Pasture 42.6 79.9 93.1

 Range 93.7 105.9 108.1

 Total 200.0 305.5 351.4

Opportunity costs: Billion dollars per year

 Crops 1.7 5.7 9.5

 Pasture 1.2 3.8 5.4

 Range 1.9 2.7 3.0

 Total 4.8 12.3 17.9

Figure 8—Marginal costs of carbon sequestration with periodic timber harvesting by initial use ($/metric ton/year).

0 50 100 150

0 50 100 150 200 250

$/metric tons per year

Million tons carbon per year

Cropland Pasture Rangeland
18

GENERAL TECHNICAL REPORT PNW-GTR-888

cost is measured as the foregone rents plus establishment costs (or, equivalently, 

the area under the marginal cost curves in fig. 8). At $50 per ton, a large share of 

the total costs is associated with rangeland conversion. At higher carbon prices, the 

opportunity costs arise mostly from the afforestation of cropland.

Figure 9 shows the geographic distribution of afforested acreage for carbon 

prices of $50 and $150 per metric ton. The most notable feature of these results is 

that increasing the carbon price from $50 to $150 has a large effect on the afforested 

acres in the section of the United States just east of the 100th meridian and, in 

particular, the Corn Belt region. Carbon sequestration costs are relatively high in 

this area (fig. 6), but much agricultural land is available for afforestation when the 

carbon price is sufficiently high. In contrast, there is less of an effect on afforested 

acres in the western and eastern portions of the country, indicating that most of the 

opportunities for afforestation are exploited at relatively low carbon prices. 

Incorporating the Holdridge forest zone constraints greatly affects cost estimates. Without the constraints, the amount of available land increases from 490 million acres to 805 million acres, causing the annual sequestration potential to increase 

370 to 560 megatons (Mt) of carbon annually. The increase in land availability 

lowers the marginal cost of carbon sequestration (fig. 10). At a $50-per-ton carbon 

price, for instance, the sequestration supply increases from 200 to 336 Mt annually.

Comparison to Other Studies

We compare our results to those in Moulton and Richards and Lubowski et al. 

(2006). Table 3 lists, for each study, the total carbon sequestered annually at different carbon prices. For a given carbon price, a larger value for total sequestered 

carbon indicates that the marginal cost curve lies farther to the right. At a price 

of $25 per ton, the estimates from this study are similar to those in Lubowski et 

al. (2006) (125 million tons compared to 113 million tons). Those in Moulton and 

Richards are similar to our estimates without the Holdridge constraints (225 million 

tons compared to 200 million tons). At a price of $50 per ton, however, the Moulton 

and Richards estimate diverges considerably from those in the other studies. 

Although there are many factors involved, two important ones are that Moulton 

and Richards assume a large stock of land available for afforestation and relatively 

low opportunity costs. In particular, the Moulton and Richards estimates do not 

account for foregone rents from future development. The results from this study 

closely match those in Lubowski et al. (2006) up to a carbon price of $75 per ton, 

but after that point, our marginal cost curve turns up, whereas the one in Lubowski 

et al. (2006) remains relatively flat. There are a number of potential reasons for this 

divergence. First, unlike Lubowski et al., we include Holdridge constraints (our 

estimates without Holdridge constraints continue to track those in Lubowski et al. 

Incorporating the 

Holdridge forest 

zone constraints 

greatly affects cost 

estimates. Without 

the constraints, the 

amount of available 

land increases from 

490 million acres to 

805 million acres, 

causing the annual 

sequestration potential 

to increase 370 to 

560 megatons (Mt) of 

carbon annually.
19

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

$150/metric ton

$50/metric ton

A

A

Figure 9—Distribution of afforested acres at carbon prices of $50 and $150 per metric ton.
20

GENERAL TECHNICAL REPORT PNW-GTR-888

(2006) up to a price of about $100 per ton). Second, our estimates of tree planting 

costs are likely to be higher than those in Lubowski et al. (2006), which are derived 

implicitly from observed land use changes and, therefore, may partially reflect costs 

of establishing forests through natural regeneration. Third, our study only accounts 

for afforestation, while Lubowski et al. (2006) also include avoided deforestation. 

Finally, it is important to note that the Lubowski et al. (2006) estimates are based on 

an econometric model of land use change, and carbon prices above $100 per ton are 

likely to be outside the range of the historical data used to estimate the model.

Figure 10—Effect of Holdridge Forest Zone constraints on marginal cost estimates. 

Table 3—Alternative estimates of total carbon sequestered (million tons 

per year) at different carbon prices

Carbon prices (per ton)

$25 $50 $75 $100 $125 $150 

This study 125.0 200.0 280.0 305.5 340.0 351.4

This study (without 200.0 345.0 440.0 480.0 510.0 530.0

 Holdridge constraints)

Moulton and Richardsa 225.0 800.0 

Lubowski et al. 113.0 200.0 323.0 575.0 800.0 1050.0

a Moulton and Richards (1990) costs adjusted to 1997 values using the Consumer Price Index.

0 200 400 600

0 50 100 150 200 250

$/metric tons per year

Million tons of carbon per year

With Holdridge constraint Without Holdridge constraint
21

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

Limitations of This Study 

As noted in the “Introduction,” to keep this analysis simple and transparent, we do 

not account for a number of factors included in recent cost studies, such as endogenous price feedbacks. We did, however, account for option value associated with 

conversion of land to developed uses. In this way, our approach includes elements 

of both bottom-up engineering studies and econometric analyses. For the factors 

that we did not account for, we can make comparisons between studies to infer 

the magnitude of these effects. Stavins and Richards (2005) normalize the results 

from a number of U.S. carbon sequestration cost studies. Adams et al. (1993) and 

Richards et al. (1993) are two studies that account for endogenous price feedbacks 

and can be compared to Moulton and Richards, which ignores these effects. Up to 

about 200 million tons per year, the marginal costs of all three studies are similar. 

At 500 million tons, however, marginal costs in Adams et al. (1993) and Richards et 

al. (1993) are 40 to 60 percent higher than in Moulton and Richards. Lubowski et al. 

(2006) account for endogenous prices and employ an econometric approach that can 

account for unobservable factors. At 500 million tons, their estimate of marginal 

cost is approximately 100 percent higher than the estimate in Moulton and Richards 

(Stavins and Richards 2005).

These comparisons suggest that, to be on the conservative side, our marginal 

cost estimates should be doubled at higher carbon sequestration levels to adjust 

for endogenous price feedbacks and unobservable landowner behavior. Even more 

important than these factors, however, may be climatic constraints on forests. Our 

marginal cost curve with Holdridge constraints is vertical well before 500 million 

tons (fig. 10), whereas when we ignore these constraints, we obtain marginal cost 

estimates similar to those in Lubowski et al. (2006).

Our cost estimates incorporate conventional estimates of carbon storage in 

forests (Smith et al. 2006). These estimates are based on FIA data that are developed from surveys of current forests, providing national coverage. In a region such 

as the Corn Belt, where most of the productive land is in agriculture, yields from 

current forests may understate the potential yield from new forests established on 

agricultural land. For the Corn Belt region, some preliminary studies have found 

that timber yields could be as much as double those found in FIA studies. Higher 

timber yields would have the effect of reducing cost estimates for carbon sequestration, and technological trends (e.g., increased use of genetic stock improvement and 

other biotechnology advances) are likely to result in significant increases in yields 

per acre.

These comparisons 

suggest that, to be 

on the conservative 

side, our marginal 

cost estimates should 

be doubled at higher 

carbon sequestration 

levels to adjust 

for endogenous 

price feedbacks 

and unobservable 

landowner behavior.
22

GENERAL TECHNICAL REPORT PNW-GTR-888

Conclusions

We provide a new set of estimates of the costs to private landowners of sequestering 

carbon by converting nonforest (rural) land to forests in the United States in order 

to enhance policy analyses of climate change mitigation options involving afforestation opportunities. The new data set includes coverage of afforestation opportunities on rangeland, new estimates of opportunity costs of nonforest land uses (using 

information on annual net returns adjusted for information derived from land value 

data and constraints on forest establishment potential), estimates of forest establishment (conversion) costs, and estimates of the foregone option to future urban 

development. The data set is county level, differentiated by land use category, and 

considers the role of possible timber harvests in reducing the net costs. 

At a carbon price of $50/ton, we estimate that approximately 200 million tons 

of carbon would be sequestered annually through afforestation. According to 

estimates in Lubowski et al. (2006), this corresponds to roughly one-half of the 

carbon abatement that would be undertaken in the U.S. energy sector at this price. 

If the carbon price were to rise to $100/ton, an additional 100 million tons of carbon 

would be sequestered each year, just over one-half of energy-based carbon abatement. Compared to earlier carbon sequestration costs studies, our estimates indicate 

a smaller, but still important, role for forest-based carbon sequestration in offsetting 

U.S. greenhouse gas emissions.

Future investigations of long-term carbon sequestration programs must grapple 

with the possibility of climate change, even if avoiding climate change is the 

intention of the policy. Many components of our cost estimates would likely change 

in the future as the result of changes in climate, including commodity and carbon 

yields, the location of Holdridge forest zones, costs of forest establishment, and 

prices for timber, crops, and other commodities. A next step in this research would 

be to modify these cost components to determine how climate change would affect 

the level and geographic distribution of future carbon sequestration costs, using, for 

example, the International Panel on Climate Change scenarios of climate change. In 

a related study, Haim et al. (2010) explored how changes in forest and agricultural 

yields and agricultural commodity prices would affect future patterns of land use in 

the United States. Improved afforestation data by region would be beneficial. In our 

analysis, we implicitly treat counties as homogenous units where variables for each 

county (soil productivity, land values, etc.) are defined at the mean. This accounts 

for heterogeneity much more than in many earlier studies, though more could be 

done as part of further work (e.g., afforestation yields across soil types, other land 

conditions, and forest management intensities by region).
23

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

Other future research and sensitivity analyses could investigate policy design 

issues and effects on costs of sequestration. Our estimates of the marginal costs of 

carbon sequestration in forest are based on the private opportunity costs of the land, 

and impacts on costs of afforestation policies by ownership type from government 

actions warrant further research, e.g., government subsidy programs for agriculture. 

In practice, there may be additional opportunity costs depending on the design of 

the carbon sequestration policy. If the government uses subsidies, for example, to 

encourage afforestation, then landowners may capture additional rents beyond the 

opportunity costs reflected in the cost estimates presented here. These payments to 

landowners can have opportunity costs if, for example, there are deadweight losses 

associated with the taxes levied to raise public funds. Mason and Plantinga (2013) 

found significant efficiency gains from the use of a contracting scheme that limits 

government payments for carbon sequestration. This discussion raises the broader 

issue of policy design, which our study does not directly address. The problems of 

additionality, permanence, and leakage have been widely discussed in the context 

of policies for carbon sequestration in forests (e.g., Richards and Andersson 2001). 

These problems are now understood to be a symptom of the project-by-project 

approach to accounting for carbon credits. Plantinga and Richards (2010) discuss 

how national-level carbon accounting can be used to remedy these problems in 

the context of an international climate change treaty. Mason and Plantinga (2013) 

and Horowitz and Just (2013) examined policies that can address additionality in a 

domestic policy setting.

Finally, future research can include broadening consideration of costs and benefits. Although many factors can increase cost estimates for carbon sequestration, 

consideration of co-benefits can effectively reduce costs. Establishing new forests 

on agricultural lands can provide wildlife habitat, and improve water quality by preventing erosion and reducing use of agricultural chemicals, among other benefits. 

Co-benefits of carbon sequestration have been estimated in earlier studies (e.g., 

Matthews et al. 2002, Plantinga and Wu 2003) for specific regions in the United 

States. Future work could focus on using these cost estimates in a national-scale 

evaluation of co-benefits. Withey et al. (2012) have begun this work by examining 

the returns to land conservation in terms of species preservation.

Establishing new 

forests on agricultural 

lands can provide 

wildlife habitat, and 

improve water quality 

by preventing erosion 

and reducing use of 

agricultural chemicals, 

among other benefits.
24

GENERAL TECHNICAL REPORT PNW-GTR-888

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New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

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GENERAL TECHNICAL REPORT PNW-GTR-888

Appendix

To access county-level data on land prices, tree establishment costs, carbon 

uptake rates, and eligible land for conversion to forest, follow this link: 

http://www.fs.fed.us/pnw/pubs/pnw_gtr888/county-level-data_nielsen2013.xlsx

Figure 11—Average crop price per acre, by county. Note: Areas with zero acreage in crops, according 

to the 1997 Natural Resources Inventory, are shown in white.

Figure 12—Average pasture price per acre, by county. Note: Areas with zero acreage in pasture, 

according to the 1997 Natural Resources Inventory, are shown in white.
29

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

Figure 13—Average range price per acre, by county. Note: Areas with zero acreage in range, according to the 1997 Natural Resources Inventory, are shown in white.

Figure 14—Average crop price per acre net of harvesting revenues, by county. Note: Areas with zero 

acreage in crops, according to the 1997 Natural Resources Inventory, are shown in white.
30

GENERAL TECHNICAL REPORT PNW-GTR-888

Figure 15—Average pasture price per acre net of harvesting revenues, by county. Note: Areas with zero acreage in pasture, according to the 1997 Natural Resources Inventory, are shown in white.

Figure 16—Average range price per acre net of harvesting revenues, by county. Note: Areas with zero acreage 

in range, according to the 1997 Natural Resources Inventory, are shown in white.
31

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

Figure 17—The share of each county in crops and a Holdridge forest zone. Note: The definition of cropland is 

based on the 2001 National Land Cover Database.

Figure 18—The share of each county in pasture and a Holdridge forest zone. Note: The definition of pasture is 

based on the 2001 National Land Cover Database.
32

GENERAL TECHNICAL REPORT PNW-GTR-888

Figure 19—The share of each county in range and a Holdridge forest zone. Note: The definition of range land 

is based on the 2001 National Land Cover Database categories. West of the 100th meridian, we define range 

by categories 71 (grassland/herbaceous) and 52 (shrub/scrub) and, east of 100th meridian, only by category 71. 

Unlike in figures 13 and 16, this definition implies positive acres of range east of the 100th meridian.
33

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

Figure 20—Average carbon sequestration costs on cropland ($/metric ton): (A) without harvesting, 

(B) with harvesting.
34

GENERAL TECHNICAL REPORT PNW-GTR-888

Figure 21—Average carbon sequestration costs on pasture land ($/metric ton): (A) without harvesting, 

(B) with harvesting.
35

New Cost Estimates for Carbon Sequestration Through Afforestation in the United States

Figure 22—Average carbon sequestration costs on rangeland ($/metric ton): (A) without harvesting, 

(B) with harvesting. Note: Rangeland is defined using 2001 National Land Cover Database categories. 

See note to figure 19 for more details.

Pacific Northwest Research Station

Web site http://www.fs.fed.us/pnw/

Telephone (503) 808–2592

Publication requests (503) 808–2138

FAX (503) 808–2130

E-mail pnw_pnwpubs@fs.fed.us

Mailing address Publications Distribution

Pacific Northwest Research Station

P.O. Box 3890

Portland, OR 97208–3890
U.S. Department of Agriculture

Pacific Northwest Research Station

333 SW First Avenue

P.O. Box 3890

Portland, OR 97208–3890

Official Business

Penalty for Private Use, $300