Terrestrial ecosystems, which store more carbon than the atmosphere, are vital to influencing carbon dioxide-driven climate change. The InVEST Carbon Storage and Sequestration model uses maps of land use along with stocks in four carbon pools (aboveground biomass, belowground biomass, soil, and dead organic matter) to estimate the amount of carbon currently stored in a landscape or the amount of carbon sequestered over time. Optionally, the market or social value of sequestered carbon, its annual rate of change, and a discount rate can be used to estimate the value of this ecosystem service to society. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates.
Ecosystems regulate Earth’s climate by adding and removing greenhouse gases (GHGs) such as CO2 from the atmosphere. Forests, grasslands, peat swamps, and other terrestrial ecosystems collectively store much more carbon than does the atmosphere (Lal 2002). By storing this carbon in wood, other biomass, and soil, ecosystems keep CO2 out of the atmosphere, where it would contribute to climate change. Beyond just storing carbon, many systems also continue to accumulate it in plants and soil over time, thereby “sequestering” additional carbon each year. Disturbing these systems with fire, disease, or vegetation conversion (e.g., land use/land cover (LULC) conversion) can release large amounts of CO2. Other management changes, like forest restoration or alternative agricultural practices, can lead to the storage of large amounts of CO2. Therefore, the ways in which we manage terrestrial ecosystems are critical to regulating our climate.
Terrestrial-based carbon sequestration and storage is perhaps the most widely recognized of all ecosystem services (Stern 2007, IPCC 2006, Canadell and Raupach 2008, Capoor and Ambrosi 2008, Hamilton et al. 2008, Pagiola 2008). The social value of a sequestered ton of carbon is equal to the social damage avoided by not releasing the ton of carbon into the atmosphere (Tol 2005, Stern 2007). Calculations of social cost are complicated and controversial (see Weitzman 2007 and Nordhaus 2007b), but have resulted in value estimates that range from USD $9.55 to $84.55 per metric ton of CO2 released into the atmosphere (Nordhaus 2007a and Stern 2007, respectively).
Managing landscapes for carbon storage and sequestration requires information about how much and where carbon is stored, how much carbon is sequestered or lost over time, and how shifts in land use affect the amount of carbon stored and sequestered over time. Since land managers must choose among sites for protection, harvest, or development, maps of carbon storage and sequestration are ideal for supporting decisions influencing these ecosystem services.
Such maps can support a range of decisions by governments, NGOs, and businesses. For example, governments can use them to identify opportunities to earn credits for reduced (carbon) emissions from deforestation and degradation (REDD). Knowing which parts of a landscape store the most carbon would help governments efficiently target incentives to landowners in exchange for forest conservation. Additionally, a conservation NGO may wish to invest in areas where high levels of biodiversity and carbon sequestration overlap (Nelson et al. 2008). A timber company may also want to maximize its returns from both timber production and REDD carbon credits (Plantinga and Birdsey 1994).
Carbon storage on a land parcel largely depends on the sizes of four carbon pools: aboveground biomass, belowground biomass, soil, and dead organic matter. The InVEST Carbon Storage and Sequestration model aggregates the amount of carbon stored in these pools according to land use maps and classifications provided by the user. Aboveground biomass comprises all living plant material above the soil (e.g., bark, trunks, branches, leaves). Belowground biomass encompasses the living root systems of aboveground biomass. Soil organic matter is the organic component of soil, and represents the largest terrestrial carbon pool. Dead organic matter includes litter as well as lying and standing dead wood.
Using maps of LULC classes and the amount of carbon stored in carbon pools, this model estimates the net amount of carbon stored in a land parcel over time and the market value of the carbon sequestered in remaining stock. Limitations of the model include an oversimplified carbon cycle, an assumed linear change in carbon sequestration over time, and potentially inaccurate discounting rates. Biophysical conditions important for carbon sequestration such as photosynthesis rates and the presence of active soil organisms are also not included in the model.
The model maps carbon storage densities to LULC rasters which may include classess such as forest, pasture, or agricultural land. The model summarizes results into raster outputs of storage, sequestration, and value, as well as aggregate totals.
For each LULC type, the model requires an estimate of the amount of carbon in at least one of the four fundamental pools described above. If you have data for more than one pool, the modeled results will be more complete. The model simply applies these estimates to the LULC map to produce a map of carbon storage in the carbon pools included.
If you provide both a current and future LULC map, then the net change in carbon storage over time (sequestration and loss) and its social value can be calculated. To estimate this change in carbon sequestration over time, the model is simply applied to the current landscape and a projected future landscape, and the difference in storage is calculated, pixel by pixel. If multiple future scenarios are available, the differences between the current and each alternate future landscape can be compared.
Additionally if you provide a REDD scenario landcover map, the model will treat that raster as an additional future scenario, calculate storage and sequestration, and summarize results.
Outputs of the model are expressed as Mg of carbon per pixel, and if desired, the value of sequestration in currency units per pixel. We strongly recommend using the social value of carbon sequestration if you are interested in expressing sequestration in monetary units. The social value of a sequestered ton of carbon is the social damage avoided by not releasing that ton of carbon into the atmosphere.
The valuation model estimates the economic value of sequestration (not storage) as a function of the amount of carbon sequestered, the monetary value of each unit of carbon, a monetary discount rate, and the change in the value of carbon sequestration over time. Thus, valuation can only be done in the carbon model if you have a future scenario. Valuation is applied to sequestration, not storage, because market prices relate only to carbon sequestration. Discount rates are multipliers that typically reduce the value of carbon sequestration over time. The first type of discounting, the standard economic procedure of financial discounting, reflects the fact that people typically value immediate benefits more than future benefits due to uncertainty and assumed economic inflation over time. The second discount rate adjusts the social value of carbon sequestration over time. This value will change as the impact of carbon emissions on expected climate change-related damages changes. If we expect carbon sequestered today to have a greater impact on climate change mitigation than carbon sequestered in the future this second discount rate should be positive. On the other hand, if we expect carbon sequestered today to have less of an impact on climate change mitigation than carbon sequestered in the future, this second discount rate should be negative.
The value of carbon sequestration over time for a given parcel x is:
\(V\) is the price per metric ton of carbon
\(s_x\) is the amount of carbon, in metric tons, sequestered on parcel \(x\)
\(q\) is the future year
\(p\) is the current year
\(r\) is the yearly market discount rate for the carbon price
\(c\) is the yearly rate of change in the price of carbon
The carbon model can optionally perform scenario analysis according to a framework of Reducing Emissions from Forest Degradation and Deforestation (REDD) or REDD+. REDD is a scheme for emissions reductions under which countries that reduce emissions from deforestation can be financially compensated. REDD+ builds on the original REDD framework by also incorporating conservation, sustainable forest management, and enhancement of existing carbon stocks.
To perform REDD scenario analysis, the model requires three LULC maps: one for the current scenario, one for a future baseline scenario, and one for a future scenario under a REDD policy. The future baseline scenario is used to compute a reference level of emissions against which the REDD scenario can be compared. Depending on the specifics on the desired REDD framework, the baseline scenario can be generated in a number of different ways; for instance, it can be based on historical rates of deforestation or on projections. The REDD policy scenario map reflects future LULC under a REDD policy to prevent deforestation and enhance carbon sequestration.
Based on these three LULC maps for current, baseline, and REDD policy scenarios, the carbon biophysical model produces rasters for total carbon storage for each of the three LULC maps, and two sequestration rasters for future and REDD scenarios.
The model simplifies the carbon cycle which allows it to run with relatively little information, but also leads to important limitations. For example, the model assumes that none of the LULC types in the landscape are gaining or losing carbon over time. Instead it is assumed that all LULC types are at some fixed storage level equal to the average of measured storage levels within that LULC type. Under this assumption, the only changes in carbon storage over time are due to changes from one LULC type to another. Therefore, any pixel that does not change its LULC type will have a sequestration value of 0 over time. In reality, many areas are recovering from past land use or are undergoing natural succession. The problem can be addressed by dividing LULC types into age classes (essentially adding more LULC types), such as three ages of forest. Then, parcels can move from one age class to the other in scenarios and change their carbon storage values as a result.
A second limitation is that because the model relies on carbon storage estimates for each LULC type, the results are only as detailed and reliable as the LULC classification used and carbon pool values supplied. Carbon storage clearly differs among LULC types (e.g., tropical forest vs. open woodland), but often there can also be significant variation within an LULC type. For example, carbon storage within a “tropical moist forest” is affected by temperature, elevation, rainfall, and the number of years since a major disturbance (e.g., clear-cut or forest fire). The variety of carbon storage values within coarsely defined LULC types can be partly recovered by using an LULC classification system and related carbon pool table which stratifies coarsely defined LULC types with relevant environmental and management variables. For example, forest LULC types can be stratified by elevation, climate bands or time intervals since a major disturbance. Of course, this more detailed approach requires data describing the amount of carbon stored in each of the carbon pools for each of the finer LULC classes.
Another limitation of the model is that it does not capture carbon that moves from one pool to another. For example, if trees in a forest die due to disease, much of the carbon stored in aboveground biomass becomes carbon stored in other (dead) organic material. Also, when trees are harvested from a forest, branches, stems, bark, etc. are left as slash on the ground. The model assumes that the carbon in wood slash “instantly” enters the atmosphere.
Finally, while most sequestration follows a nonlinear path such that carbon is sequestered at a higher rate in the first few years and a lower rate in subsequent years, the model’s valuation of carbon sequestration assumes a linear change in carbon storage over time. Due to discounting, the assumption of a constant rate of change will tend to undervalue sequestered carbon, as a nonlinear path of sequestration is more socially valuable than is a linear path (Figure 1).
Figure 1: The model assumes a linear change in carbon storage (the solid line), while the actual path to the year “T“‘s carbon storage level may be non-linear (like the dotted line). In this case “t” indicates the year of the current landscape and “T” the year of the future landscape. With positive discounting, the value of the modeled path (the solid line) is less valuable than the actual path. Therefore, if sequestration paths tend to follow the dotted line, the model will undervalue sequestered carbon.
All spatial inputs must be in the same projected coordinate system and in linear meter units.
All carbon data should be for elemental carbon, not CO2.
Current LULC (raster, required): A map of LULC for the current scenario. All values in this raster must have corresponding entries in the Carbon Pools table.
Current LULC Year (number, units: year, conditionally required): The calendar year of the current scenario depicted in the current LULC map. Required if Run Valuation model is selected.
Calculate Sequestration (true/false): Run sequestration analysis. This requires inputs of LULC maps for both current and future scenarios. Required if REDD scenario analysis or run valuation model is selected.
Future LULC (raster, conditionally required): A map of LULC for the future scenario. If run valuation model is selected, this should be the reference, or baseline, future scenario against which to compare the REDD policy scenario. All values in this raster must have corresponding entries in the Carbon Pools table. Required if Calculate Sequestration is selected.
Future LULC Year (number, units: year, conditionally required): The calendar year of the future scenario depicted in the future LULC map. Required if Run Valuation model is selected.
REDD Scenario Analysis (true/false): Run REDD scenario analysis. This requires three LULC maps: one for the current scenario, one for the future baseline scenario, and one for the future REDD policy scenario.
REDD LULC (raster, conditionally required): A map of LULC for the REDD policy scenario. All values in this raster must have corresponding entries in the Carbon Pools table. Required if REDD Scenario Analysis is selected.
Carbon Pools (CSV, required): A table that maps each LULC code to carbon pool data for that LULC type. If information on some carbon pools is not available, pools can be estimated from other pools, or omitted by leaving all values for the pool equal to 0.
Example: Hypothetical study with five LULC classes. Class 1 (Forest) contains the most carbon in all pools. In this example, carbon stored in above- and below-ground biomass differs strongly among land use classes, but carbon stored in soil varies less dramatically.
Run Valuation Model (true/false): Calculate net present value for the future scenario, and the REDD scenario if provided, and report it in the final HTML document.
Price of Carbon (number, units: currency/t, conditionally required): The present value of carbon. Required if Run Valuation model is selected. This is \(V\) in equation (5). Price given in currency (any currency) per metric ton of elemental carbon (not CO2). For applications interested in estimating the total value of carbon sequestration, we recommend value estimates based on damage costs associated with the release of an additional ton of carbon - the social cost of carbon (SCC). Stern (2007), Tol (2009), and Nordhaus (2007a) present estimates of SCC. For example, two SCC estimates we have used from Tol (2009) are $66 and $130 (in 2010 US dollars) (Polasky et al. 2010).
Annual Market Discount Rate (ratio, conditionally required): The annual market discount rate in the price of carbon, which reflects society’s preference for immediate benefits over future benefits. Required if Run Valuation model is selected. This is \(r\) in equation (5). One default value is 7% per year, which is one of the market discount rates recommended by the U.S. government for cost-benefit evaluation of environmental projects. However, this rate will depend on the country and landscape being evaluated, and should be selected based on local requirements. Philosophical arguments have been made for using a lower discount rate when modeling climate change related dynamics, which users may consider using. If the rate is set equal to 0% then monetary values are not discounted.
Annual Price Change (ratio, conditionally required): The relative annual increase of the price of carbon. Required if Run Valuation model is selected. This is \(c\) in equation (5). This adjusts the value of sequestered carbon as the impact of emissions on expected climate change-related damages changes over time.
Setting this rate greater than 0% suggests that the societal value of carbon sequestered in the future is less than the value of carbon sequestered now. It has been widely argued that GHG emissions need to be curtailed immediately to avoid crossing a GHG atmospheric concentration threshold that would lead to a 3 degree Celsius or greater change in global average temperature by 2105. Some argue that such a temperature change would lead to major disruptions in economies across the world (Stern et al. 2006). Therefore, any mitigation in GHG emissions that occurs many years from now may have no effect on whether or not this crucial concentration threshold is passed. If this is the case, C sequestration in the far future would be relatively worthless and a carbon discount rate greater than zero is warranted.
Alternatively, setting the annual rate of change less than 0% (e.g., -2%) suggests that the societal value of carbon sequestered in the future is greater than the value of carbon sequestered now (this is a separate issue than the value of money in the future, a dynamic accounted for with the market discount rate). This may be the case if the damages associated with climate change in the future accelerate as the concentration of GHGs in the atmosphere increases.
Parameter log: Each time the model is run, a text (.txt) file will be created in the Workspace. The file will list the parameter values and output messages for that run and will be named according to the service, the date and time. When contacting NatCap about errors in a model run, please include the parameter log.
report_[Suffix].html: This file presents a summary of all data computed by the model. It also includes descriptions of all other output files produced by the model, so it is a good place to begin exploring and understanding model results. Because this is an HTML file, it can be opened with any web browser.
tot_c_cur_[Suffix].tif/tot_c_fut_[Suffix].tif/tot_c_redd_[Suffix].tif: Rasters showing the amount of carbon stored in Mg in each pixel for the current, future, and REDD scenarios. It is a sum of all of the carbon pools provided by the biophysical table.
delta_cur_fut_[Suffix].tif/delta_cur_redd_[Suffix].tif: Rasters showing the difference in carbon stored between the future/REDD landscape and the current landscape. The values are in Mg per pixel. In this map some values may be negative and some positive. Positive values indicate sequestered carbon, negative values indicate carbon that was lost.
npv_fut_[Suffix].tif/npv_redd_[Suffix].tif:** Rasters showing the economic value of carbon sequestered between the current and the future/REDD landscape dates. The units are in currency per pixel.
c_above_[Suffix].tif: Raster of aboveground carbon values, mapped from the Carbon Pools table to the LULC.
c_below_[Suffix].tif: Raster of belowground carbon values, mapped from the Carbon Pools table to the LULC.
c_dead_[Suffix].tif: Raster of dead carbon values, mapped from the Carbon Pools table to the LULC.
c_soil_[Suffix].tif: Raster of soil carbon values, mapped from the Carbon Pools table to the LULC.
_tmp_work_tokens: This directory stores metadata used internally to enable avoided re-computation. No model results are stored here.
Recent estimates suggest that the social cost of carbon (SCC), or the marginal damage associated with the release of an additional Mg of C into the atmosphere, ranges from $32 per metric ton of C (Nordhaus 2007a) to $326 per metric ton of C (Stern 2007) in 2010 US dollars. The value of this damage can also be considered the monetary benefit of an avoided release. Tol (2009) provides a comprehensive survey of SCC estimates, reporting median values of $66 and $130 per metric ton in 2010 US dollars (values differ because of different assumptions regarding discounting of time). Other estimates can be found in Murphy et al. (2004), Stainforth et al. (2005), and Hope (2006).
An alternative method for measuring the cost of an emission of a metric ton of C is to set the cost equal to the least cost alternative for sequestering that ton. The next best alternative currently is to capture and store the C emitted from utility plants. According to Socolow (2005) and Socolow and Pacala (2007), the cost of this technology per metric ton captured and stored is approximately $100.
Finally, while we do not recommend this approach, market prices can be used to set the price of sequestered carbon. We do not recommend the use of market prices because they usually only apply to “additional” carbon sequestration; sequestration above and beyond some baseline sequestration rate. Further, carbon credit values from carbon markets are largely a function of various carbon credit market rules and regulations and do not necessarily reflect the benefit to society of a sequestered ton of carbon. Therefore, correct use of market prices would require estimating a baseline rate for the landscape of interest, mapping additional sequestration, and then determining which additional sequestration is eligible for credits according to market rules and regulations.
We discount the value of future payments for carbon sequestration to reflect society’s preference for payments that occur sooner rather than later. The U.S. Office of Management and Budget recommends a 7% per annum market discount rate for US-based projects (OMB 1992). Discount rates vary for other parts of the world. Canada and New Zealand recommend 10% for their projects (Abusah and de Bruyn 2007). It is best to look for the recommended discount rate for your country.
Some economists believe that a market or consumption discount rate of 7% to 12% is too high when dealing with the climate change analysis. Because climate change has the potential to severely disrupt economies in the future, the preference of society to consume today at the expense of both climate stability in the future and future generations’ economic opportunities is seen as unethical by some (Cline 1992, Stern 2007). According to this argument, analyses of the effects of climate change on society and policies designed to reduce climate change should use low discount rates to encourage greater GHG emission mitigation and therefore compensate for the potentially severe damages incurred by future generations (e.g., r = 0.014 in Stern (2007)). Recent government policies in several countries have supported the use of a very low discount rate for certain long-term projects (Abusah and de Bruyn 2007).
The carbon discount rate, which reflects the greater climatic impact of carbon sequestered immediately over carbon sequestered in the future, is discussed in Adams et al. (1999), Plantinga et al. (1999), Feng 2005, and Nelson et al. (2008).
Abusah, Sam and Bruyn, Clinton de. 2007. Getting Auckland on Track: Public Transport and New Zealand’s Economic. Ministry of Economic Development Working Paper. Accessed at <http://s3.amazonaws.com/zanran_storage/www.med.govt.nz/ContentPages/4013253.pdf>.
Adams, DM, RJ Alig, BA McCarl, et al. 1999. Minimum cost strategies for sequestering carbon in forests. Land Econ 75: 360-374.
Anderson, JR, EE Hardy, JT Roach, RE Witmer. A Land Use and Land Cover Classification System for Use with Remote Sensor Data. Washington, DC: United States Government Printing Office; 1976. Geological Survey Professional Paper 964.
Antle, JM, and B. Diagana. 2003. Creating Incentives for the Adoption of Sustainable Agricultural Practices in Developing Countries: The Role of Soil Carbon Sequestration. American Journal of Agricultural Economics 85:1178-1184.
Baer, SG, DJ Kitchen, JM Blair, and CW Rice. 2002. Changes in Ecosystem Structure and Function along a Chronosequence of Restored Grasslands. Ecological Applications 12:1688-1701.
Bernoux, M., MDS Carvalho, B. Volkoff, and CC Cerri. 2002. Brazil’s soil carbon stocks. Soil Science Society of America Journal 66:888-896.
Brown, SL, PE Schroeder and JS Kern. Spatial distribution of biomass in forests of the eastern USA.Forest Ecology and Management 123 (1999) 81-90.
Brown, S. 2002. Measuring carbon in forests: current status and future challenges. Environmental Pollution 116:363-372.
Brown, S. Estimating Biomass and Biomass Change of Tropical Forests: a Primer. FAO Forestry Department; 1997. Report for FAO Forestry Paper 134.
Brown, S. and PE Schroeder. 1999. Spatial patterns of aboveground production and mortality of woody biomass for eastern US forests. Ecological Applications 9:968-980.
Cairns, MA, PK Haggerty, R. Alvarez, BHJ De Jong, and I. Olmsted. 2000. Tropical Mexico’s recent land-use change: A region’s contribution to the global carbon cycle. Ecological Applications 10:1426-1441.
Cairns, MA, S. Brown, EH Helmer, and GA Baumgardner. 1997. Root biomass allocation in the world’s upland forests. Oecologia 111:1-11.
Canadell, JG and MR Raupach. 2008. Managing Forests for Climate Change Mitigation. Science 320:1456-1457.
Cline, WR. 1992. The economics of global warming. Instuitute for International Economics, Washington, D.C.
Coomes, DA, RB Allen, NA Scott, C. Goulding, and P. Beets. 2002. Designing systems to monitor carbon stocks in forests and shrublands. Forest Ecology and Management 164:89-108.
Conte, MN and MJ Kotchen. 2010. Explaining the price of voluntary carbon offsets. Climate Change Economics 1 (2):93-111.
Capoor, K., and P. Ambrosi. State and Trends of the Carbon Market 2008. Washington, D.C.: World Bank Institute, 2008 May.
Delaney, M., S. Brown, AE Lugo, A. Torres-Lezama, and NB Quintero. 1998. The quantity and turnover of dead wood in permanent forest plots in six life zones of Venezuela. Biotropica 30:2-11.
Detwiler, RP. 1986. Land Use Change and the Global Carbon Cycle: The Role of Tropical Soils. Biogeochemistry 2:67-93.
Edinburgh Centre for Carbon Management. The Establishing Mechanisms for Payments for Carbon Environmental Services in the Eastern Arc Mountains, Tanzania; 2007 May 2007.
Fargione, J., J. Hill, D. Tilman, S. Polasky, and P. Hawthorne. 2008. Land Clearing and the Biofuel Carbon Debt. Science 319:1235-1238.
Feng, H. 2005. The dynamics of carbon sequestration and alternative carbon accounting, with an application to the upper Mississippi River Basin. Ecological Economics 54:23-35.
Gaston, G., S. Brown, M. Lorenzini, and KD Singh. 1998. State and change in carbon pools in the forests of tropical Africa. Global Change Biology 4:97-114.
Glenday, J. 2006. Carbon storage and emissions offset potential in an East African tropical rainforest. Forest Ecology and Management 235:72-83.
Grace, J., J. San Jose, P. Meir, HS Miranda, and RA Montes. 2006. Productivity and carbon fluxes of tropical savannas. Journal of Biogeography 33:387-400.
Gibbs, HK, S Brown, JO Niles, and JA Foley. 2007. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters 2:045023.
Hamilton, K., M Sjardin, T Marcello, and G Xu. Forging a Frontier: State of the Voluntary Carbon Markets 2008. Washington, D.C.: Ecosystem Marketplace and New Carbon Finance; 2008.
Hope, CW. 2006. The social cost of carbon: what does it actually depend on? Climate Policy 6: 565–572
Houghton, RA. 2005. Tropical deforestation as a source of greenhouse gas emissions. In: Tropical Deforestation and Climate Change, Moutinho and Schwartzman [eds.]. Instituto de Pesquisa Ambiental da Amazonia and Environmental Defense, Belem, Brazil.
Houghton, RA, and JL Hackler. 2006. Emissions of carbon from land use change in sub-Saharan Africa. Journal of Geophysical Research 111.
The Intergovernmental Panel on Climate Change (IPCC). 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 4: Agriculture, Forestry and Other Land Use. Prepared by the National Greenhouse Gas Inventories Programme, Eggleston, HS, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds). Institute for Global Environmental Strategies (IGES), Hayama, Japan. <https://www.ipcc-nggip.iges.or.jp/public/2006gl/vol4.html>.
Jenny, H. 1980. The Soil Resource. Springer, New York.
Lal, R. 2004. Soil Carbon Sequestration Impacts on Global Climate Change and Food Security. Science 304:1623-1627.
Mackey, B, Keith H, Berry S.L, Lindenmayer DB. Green carbon: the role of natural forests in carbon storage. Part 1, A green carbon account of Australia’s Southeastern Eucalypt forest, and policy implications. Canberra, Australia: ANU E Press, 2008.
Makundi, WR. 2001. Carbon mitigation potential and costs in the forest sector in Tanzania. Mitigation and Adaptation Strategies for Global Change 6:335-353.
Malhi, Y., D. Wood, TR Baker, et al. 2006. The regional variation of aboveground live biomass in old-growth Amazonian forests. Global Change Biology 12:1107-1138.
Malimbwi, RE, B. Solberg, and E. Luoga. 1994. Estimation of biomass and volume in miombo woodland at Kitungalo Forest Reserve Tanzania. Journal of Tropical Forest Science 7:230-242.
McLauchlan, KK., SE Hobbie, and WM Post. 2006. Conversion From Agriculture To Grassland Builds Soil Organic Matter On Decadal Timescales. Ecological Applications 16:143-153.
Mollicone D., F. Achard, S. Federici, H. Eva, G. Grassi, A. Belward, F. Raes, G. Seufert, H. Stibig, G. Matteucci, and E. Schulze. 2007. An incentive mechanism for reducing emissions from conversion of intact and non-intact forests. Climatic Change 83:477-493.
Munishi, PKT and TH Shear. 2004. Carbon Storage in Afromontane Rain Forests of the Eastern Arc Mountains of Tanzania: their Net Contribution to Atmospheric Carbon. Journal of Tropical Forest Science 16:78-93.
Murphy, JM et al. 2004. Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430, 768-772.
Murray, B., B. Sohngen, and M. Ross. 2007. Economic consequences of consideration of permanence, leakage and additionality for soil carbon sequestration projects. Climatic Change 80:127-143.
Nascimento, HEM, and WF Laurance. 2002. Total aboveground biomass in central Amazonian rainforests: a landscape-scale study. Forest Ecology and Management 168:311-321.
Nelson, E., G. Mendoza, J. Regetz, S. Polasky, H. Tallis, D. Cameron, K. Chan, G. Daily, J. Goldstein, P. Kareiva, E. Lonsdorf, R. Naidoo, TH Ricketts, and R. Shaw. 2009. Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Frontiers in Ecology and the Environment.
Nordhaus, W. 2007a. Critical Assumptions in the Stern Review on Climate Change. Science 317 (5835): 201–202.
Nordhaus, W. 2007b. A Review of the Stern Review on the Economics of Global Warming. Journal of Economic Literature 45: 686-702.
Pagiola, S. 2008. Payments for environmental services in Costa Rica. Ecological Economics 65 (4): 712-724.
Plantinga, AJ, and RA Birdsey. 1994. Optimal Forest Stand Management When Benefits are Derived from Carbon. Natural Resource Modeling 8(4): 373-387.
Polasky, S, E Nelson, D Pennington, and K Johnson. 2010. The Impact of Land-Use Change on Ecosystem Services, Biodiversity and Returns to Landowners: A Case Study in the State of Minnesota. Environmental and Resource Economics 48:219-242
Post, WM, WR Emanuel, PJ Zinke, and AG Stangenberger. 1982. Soil carbon pools and world life zones. Nature 298:156-159.
Post, WM, KC Kwon. 2000. Soil carbon sequestration and land-use change: processes and potential. Global Change Biology 6:317-327.
Raich, JW, AE Russell, K. Kitayama, WJ Parton, and PM Vitousek. 2006. Temperature influences carbon accumulation in moist tropical forests. Ecology 87:76-87.
Ruesch A, and HK Gibbs. 2008. New IPCC tier-1 global biomass carbon map for the year 2000. Available:https://cdiac.ess-dive.lbl.gov/epubs/ndp/global_carbon/carbon_documentation.html.
Schuman, GE, HH Janzen, and JE Herrick. 2002. Soil carbon dynamics and potential carbon sequestration by rangelands. Environmental Pollution, 116:391-396.
Sedjo, RA and B. Sohngen. Carbon Credits for Avoided Deforestation. Washington, DC: Resources for the Future; 2007 October 2007. Report for RFF DP 07-47.
Silver, WL, R. Ostertag, and AE Lugo. 2000. The potential for carbon sequestration through reforestation of abandoned tropical agricultural and pasture lands. Restoration Ecology 8:394-407.
Socolow, RH. 2005. Can We Bury Global Warming? Scientific American 293: 49-55.
Socolow, RH and SW Pacala. 2006. A Plan to Keep Carbon in Check. Scientific American 295: 50-57.
Sohngen, Brent, RH Beach, and Kenneth Andrasko. 2008. Avoided Deforestation as a Greenhouse Gas Mitigation Tool: Economic Issues. Journal of Environmental Quality 37: 1368-1375.
Stainforth, DA et al., 2005. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433, 403–406.
Stern, N. 2007. The Economics of Climate Change: The Stern Review. Cambridge and New York: Cambridge University Press.
Tiessen, H., C. Feller, EVSB Sampaio, and P. Garin. 1998. Carbon Sequestration and Turnover in Semiarid Savannas and Dry Forest. Climatic Change 40:105-117.
Tilman, D., J. Hill, and C. Lehman. 2006. Carbon-Negative Biofuels from Low-Input High-Diversity Grassland Biomass. Science 314:1598-1600.
Tol, RSJ. 2005. The marginal damage costs of carbon dioxide emissions: an assessment of the uncertainties. Energy Policy 33:2064-2074.
Tol, RSJ. 2009. The Economic Effects of Climate Change.Journal of Economic Perspectives 23:29-51.
USOMB (US Office of Management and Budget). 1992. Guidelines and Discount Rates for Benefit-Cost Analysis of Federal Programs Circular No. A-94 (Revised). Transmittal Memo No. 64. Washington DC: US Office of Management and Budget.
Vagen, TG, R Lal, and BR Singh. 2005. Soil carbon sequestration in sub-Saharan Africa: A review. Land Degradation & Development 16:53-71.
Weitzman, ML. 2007. A review of the Stern Review on the Economics of Climate Change. Journal of Economic Literature 45:703-724.
Zhang, Q, and CO Justice. 2001. Carbon Emissions and Sequestration Potential of Central African Ecosystems. AMBIO 30:351-355.