Ecology and SocietyEcology and Society
 E&S Home > Vol. 18, No. 3 > Art. 6
The following is the established format for referencing this article:
Watson, C., S. Mourato, and E. J. Milner-Gulland. 2013. Uncertain emission reductions from forest conservation: REDD in the Bale Mountains, Ethiopia. Ecology and Society 18(3): 6.
Research, part of a special feature on Ecosystem Services, Governance, and Stakeholder Participation

Uncertain Emission Reductions from Forest Conservation: REDD in the Bale Mountains, Ethiopia

1London School of Economics and Political Science, 2Imperial College London


The environmental integrity of a mechanism rewarding Reduced Emissions from Deforestation and Degradation (REDD) depends on appropriate accounting for emission reductions. Largely stemming from a lack of forest data in developing countries, emission reductions accounting contains substantial uncertainty as a result of forest carbon stock estimates, where the application of biome-averaged data over large forest areas is commonplace. Using a case study in the Bale Mountains in Ethiopia, we exemplify the implications of primary and secondary forest carbon stock estimates on predicted REDD project emission reductions and revenues. Primary data estimate area-weighted mean forest carbon stock of 195 tC/ha ± 81, and biome-averaged data reported by the Intergovernmental Panel on Climate Change underestimate forest carbon stock in the Bale Mountains by as much as 63% in moist forest and 58% in dry forest. Combining forest carbon stock estimates and uncertainty in voluntary carbon market prices demonstrates the financial impact of uncertainty: potential revenues over the 20-year project ranged between US$9 million and US$185 million. Estimated revenues will influence decisions to implement a project or not and may have profound implications for the level of benefit sharing that can be supported. Strong financial incentives exist to improve forest carbon stock estimates in tropical forests, as well as the environmental integrity of REDD projects.
Key words: deforestation; emission reductions accounting; Ethiopia; forest carbon stocks; REDD; uncertainty


Deforestation and forest degradation is driven largely by private incentives, with the benefits of ecosystem services often overlooked. A Reduced Emissions from Deforestation and Degradation (REDD) mechanism can help address this market failure by financially rewarding greenhouse gas (GHG) emission reductions from conservation, sustainable management, and forest enhancement activities (Chomitz 2007, Parker et al. 2008, Paquette et al. 2009, Mustalahti et al. 2012). A REDD mechanism under the United Nations Framework Convention on Climate Change (UNFCCC) is yet to be defined (Angelsen and McNeill 2012). Voluntary carbon markets (VCM) are the main platform through which emission reductions from forestry are currently traded (Diaz et al. 2011). The environmental integrity of REDD requires the generation of real, permanent, and verifiable emission reductions (UNDP 2009). Despite a proliferation of REDD activities, the assessment of emission reductions contains substantial uncertainty (Brown and Lugo 1992, Monni et al. 2007, Grainger 2008, Larocque et al. 2008).

In the case of avoided deforestation, emission reductions accounting requires the quantification of forest area, forest area change, and forest carbon stocks. The measurement of forest area and forest area change is being advanced through better coverage and accessibility of remote sensing imagery (Achard et al. 2004, Mayaux et al. 2005, DeFries et al. 2007, Ramankutty et al. 2007, Goetz et al. 2009, Baker et al. 2010). It is increasingly being used to infer forest carbon stocks over large spatial scales, although limitations still exist in linking imagery to on-the-ground data and in the ability to monitor forest degradation and carbon stored in deadwood and litter (Baccini et al. 2008, Baker et al. 2010, Bucki et al. 2012). Discussions are ongoing to develop and agree on methods to establish past and predicted future rates of deforestation from which the emission reductions of an intervention can be estimated (Angelsen 2008, Olander et al. 2008, Bond et al. 2009, Griscom et al. 2009, Huettner et al. 2009, Estrada 2011). In the VCM, standards set out detailed methods of good practice for establishing baselines. The Voluntary Carbon Standard is the most commonly applied, with certified projects commanding a price premium (Diaz et al. 2011, Estrada 2011). Despite advances, there remain capacity gaps in forest monitoring for REDD in countries that lack the resources and expertise to make the most in advances in satellite imagery technology or to model the drivers of deforestation (Romijn et al. 2012).

We focus on uncertainty in the estimation of forest carbon stock. Uncertainty in the carbon content of the dry biomass of a forest per unit area often results from a lack of data on key forest variables and parameters, resources, or capacity (Brown et al. 1989, Smith and Heath 2001, Andersson et al. 2009). Changes in the estimates of forest carbon stock in the FAO Forest Resource Assessment, a widely used database of global and national forest statistics, have been attributed to information availability rather than forest carbon stock change (Houghton 2005, Grainger 2008). The lack of forest monitoring data is particularly acute for Africa (FPAN 2010, Romijn et al. 2012). Reported estimates of forest carbon stocks for Africa are highly variable, from 0 to 454 tC/ha (IPCC 2006, Gibbs et al. 2007, Baccini et al. 2008). Lewis et al. (2009) estimated forest carbon stocks from permanent plots across Africa with an average of 202 tC/ha. While methods of generating forest carbon stock estimates are well accepted and tested by forest scientists, such local measurements or permanent sample plots are resource intensive and thus hard to conduct across large spatial scales (Nagendra and Ostrom 2011). As REDD grows in popularity, however, project developers and policy-makers have greater need for large-scale forest information.

The application of biome-averaged data for forest carbon stocks has become commonplace in emission reductions accounting from avoided deforestation (Brown and Gaston 1995, Gibbs et al. 2007, Djomo et al. 2010). The Intergovernmental Panel on Climate Change (IPCC) guidance for GHG inventories has three method tiers intended to promote broad engagement irrespective of a country’s data and capacity (IPCC 2003, 2006, Baker et al. 2010). Tier 3 uses advanced estimation approaches with complex models and highly disaggregated data. Tier 2 uses country-specific forest carbon stock information with activity data at small scales. Tier 1 is based on biome-averaged and default values for forest carbon stocks and contains the greatest level of uncertainty (Böttcher et al. 2009). While IPCC guidance was not designed to produce emission estimates for REDD projects, it is the basis of many REDD standards, and the UNFCCC supports the use of IPCC guidance in REDD development (UNFCCC 2009). While data used in Tier 1 are able to capture broad ecological factors influencing forest carbon stocks, such as temperature and rainfall (Chave et al. 2004, GOFC-GOLD 2008), forest heterogeneity is obscured (Bradford et al. 2010, Houghton et al. 2001). The discrepancies in forest carbon stock methods are further compounded by the need to combine forest carbon stocks with other forest variables in emission reductions accounting (Waggoner 2009, Ciais et al. 2011).

Literature is emerging on the uncertainty in forest carbon stocks and resultant implications (Houghton and et al. 2001, Houghton 2005, Mollicone et al. 2007, Ramankutty et al. 2007, Pelletier et al. 2010). A comparison across six countries by an international panel on Global Observation of Forest and Land Cover Dynamics (GOFC-GOLD 2008) indicated that biome-averaged data overestimated forest carbon stock by 33% in Mexican temperate forest and underestimated it by 44% in African rain forest when compared to primary forest data. Saatchi et al. (2011) produced a global map of forest carbon stocks based on satellite imagery and on-the-ground forest plots. Propagating errors through the estimation process, they found uncertainty in forest carbon stocks of 38% over Latin America, Sub-Saharan Africa, and Southeast Asia. By translating errors in estimating forest carbon stocks into the environmental integrity of emission reductions in Costa Rica, Kerr et al. (2004) found uncertainty is impacted by forest type, with highest uncertainty in tropical wet forest. Pelletier et al. (2010) used five carbon stock estimates for Panamanian forests in land conversion and transition models, and found 144% difference in emission reductions between the highest and lowest forest carbon stock estimate.

A discrepancy in emission reductions as a result of forest carbon stock methods could mean the difference between a decision to implement a REDD project or not, as revenues available to alter incentives for forest conservation are dependent on the market value of the emission reductions and the costs of getting them to market. REDD project feasibility studies will often combine emission reductions estimates with VCM variables such as price, implementation, and transaction costs, further compounding the uncertainty. No standardized method of assessing or communicating uncertainty in emission reductions accounting exists, and being conservative remains a dominant approach (Mollicone et al. 2007, Grassi et al. 2008). By omitting carbon pools or taking lower bounds, the principle of conservativeness ensures a low probability that carbon emission reductions are overestimated (GOFC-GOLD 2008). By assuming zero uncertainty, conservativeness can leave decision-makers without a confidence interval of the emission reductions estimate (Andersson et al. 2009). With biome-averaged forest carbon stocks often applied in REDD feasibility studies and more complex accounting methods applied during project development, conservativeness may result in missed climate change mitigation opportunities if emission reductions are more substantial than a feasibility assessment indicates (Shoch et al. 2011). Discrepancies in emission reductions between feasibility study and project development can also erode the credibility of a REDD project, questioning its environmental integrity. Unrealistic expectations can be ameliorated if uncertainty can be quantified, reduced where possible, and communicated more appropriately (Waggoner 2009, Baker et al. 2010).

We demonstrate the financial significance of forest carbon stock methods using a proposed REDD project in the Bale Mountains of Ethiopia. Aiming to highlight the implications of the uncertainty in forest carbon stocks, we explore the emission reductions and potential REDD revenues and profits under three forest carbon stock estimates and two carbon market prices. The implications for project implementation at the case study site are then addressed. We add to current knowledge through the estimation of forest carbon stock in the Bale Mountains. We build on a limited literature on the environmental integrity of REDD and the financial implications of forest carbon stock methods, and make recommendations for improving forest data, with an overarching aim to aid REDD policy decisions.


Study area

In the Oromia state of southeast Ethiopia, the Bale Mountains lie between 50˚22'–80˚08'N and 38˚41'–40˚44'E. Average annual temperature is 17.5˚C, and ranges from 10˚C to 25˚C, with average annual rainfall of 875 mm experienced in one long season between June and October, and one short season between March and May (Yimer et al. 2006). Moist tropical forest occurs between 2600 m above sea level (masl) and 1500 masl, and is characterized by Hagenia abyssinica and wild coffee (Coffea arabica). North of the plateau, habitats are comprised of dry forest, woodlands, grasslands, and wetlands, largely between 2500 masl and 3500 masl. Dry forests contain high-value commercial species such as Juniperus procera and Podocarpus falcatus as well as Prunus africanus, a threatened species. The lower altitude land of the southeast of the Bale Mountains, below 1500 masl, is dominated by acacia woodland (UNIQUE 2008, Teshome et al. 2011). The large topographical variation in the Bale Mountains is observed across wider Ethiopia, and the forest is representative of other east African montane habitats that extend through Tanzania, Kenya, and Uganda (FPAN 2010).

Rural communities in the Bale Mountains deforest to procure land for crops and livestock grazing and to meet timber and firewood needs (BERSMP 2006, BMNP 2007). This pattern is replicated over Ethiopia, with large-scale conversion of land to agriculture also playing a role at the national level (Forest Carbon Partnership Facility 2011). Between 1986 and 2009, annual deforestation in the Bale Mountains ranged from 1% to 8%, with an average rate of 3.7% (Dupuy 2009), almost four times the 1% country-wide forest loss (FAO 2010). In order to address forest decline in the Bale Mountains, a REDD project is being developed by the Oromia Regional State Forest and Wildlife Enterprise, which is supported by the Bale Eco-Region Sustainable Management Program (BERSMP), a joint nongovernmental organization program between FARM-Africa and SOS Sahel Ethiopia. The REDD project area covers 923,593 ha, including 576,856 ha of tropical dry and moist forest. To achieve a reduction in deforestation to 1% in project Year 20, participatory forest management (PFM) is being implemented across the Bale Mountains, which includes the establishment of community-based organizations, development of forest management plans, and implementation of sustainable forest management practices. PFM is regarded as a tool for REDD implementation alongside additional support for sustainable agricultural intensification, woodlot establishment, and improved fire management.

Estimating forest carbon stocks and emission reductions

Three sources of forest carbon stock information were used to model emission reductions in the Bale Mountains:
  1. Ecological zone-specific forest carbon stock from the IPCC Land Use, Land-Use Change and Forestry Good Practice Guidance (IPCC 2003)

  2. Africa-specific forest carbon stock from the IPCC Agriculture, Forestry and Other Land Use guidelines (IPCC 2006)

  3. An estimate of forest carbon stock based on field sampling in the Bale Mountains

Forest plots

Primary data collection focused on the above-ground tree carbon pool of 108 forest plots of 20 m x 20 m sampled between December 2008 and April 2010 (Fig. 1). Because this carbon pool contains the greatest fraction of total living biomass in a forest, it is most immediately impacted by deforestation and degradation (Brown 1997, FAO 2003). Plots were undertaken in tropical moist degraded forest, tropical moist nondegraded forest, and tropical dry degraded forest; no nondegraded tropical dry forest remains. Stratification of the forest area was undertaken by forestry consultants using satellite imagery and expert consultation (UNIQUE 2008). Stratification helps capture forest carbon stock clusters based on variation in factors such as elevation, temperature, precipitation, and soil fertility (Houghton 2005). While uncertainty due to errors in stratification is not dealt with in this study, it is recognized that higher resolution satellite imagery or post-stratification based on factors that affect carbon stock could reduce uncertainty in forest carbon stock estimates. Plot coordinates were selected using random number generation to identify numbered map crosshairs but were limited to logistically accessible areas for which field sampling permissions had been granted. Drawing on existing forest inventory protocol (MacDicken 1997, Pearson et al. 2005, Greenhalgh et al. 2006), the diameter at breast height (dbh) was recorded for all trees within each forest plot, with a lower limit of 5 cm dbh used to define a “tree.” Canopy cover and plot slope were also recorded. Retrospective power analysis was undertaken to illustrate the minimum number of forest plots required for the mean forest carbon stock estimate to be within an error bound of 20% of the mean with 95% probability. Logistical issues prohibited a priori calculation of the sample size; however, retrospective power analysis allowed comparison of the desired number of plots to the actual number of plots sampled for a given predictive power (Pearson et al. 2005).

Biomass regression equations

To determine forest carbon stock, the above-ground biomass was first estimated per tree by applying pan-tropical mixed species broadleaf allometric regression equations that statistically relate forest attributes to above-ground biomass (Brown 1997, Eq 3.2.1 and 3.2.4). While 95% of the variation in the above-ground biomass of trees can be explained by dbh (Brown 2002), studies indicate that using measurements of tree height and wood density—the dry weight per unit volume of wood—in allometric equations can improve biomass estimates (Brown et al. 1989, Chave et al. 2005, van Breugel et al. 2011, Marshall et al. 2012). However, it is also noted that for trees with large dbh, mechanical or physiological limits can alter the relationship between height and dbh (Chave et al. 2005). Few site and species-specific allometric equations exist for Sub-Saharan Africa, however, and only 15% of 850 Sub-Saharan African allometric equations use height (Henry et al. 2011, Shackleton and Scholes 2011). Ethiopian allometric equations are often single species, and equations are found to be of varying quality (Henry et al. 2011). Pan-tropical allometric equations were applied because no mixed species allometric equations exist for the Bale Mountains, and destructive sampling to generate equations was not feasible. The allometric equation was based on dbh and not height or wood density in light of the lack of data at the case study site and the difficulties in measuring tree height accurately in tropical forests. Chave et al. (2005) found that tree allometry is conserved across the tropics of Latin America, Southeast Asia, and Oceania, although insufficient data were available to assess if this was the case for the African continent. However, allometry is conserved only where applied to trees within the maximum and minimum tree dbh used to generate the equation. Trees with diameters that exceeded the upper limit of the range used to create the regression equations were therefore assumed to fall at the maximum dbh of the equation (148 cm). Although a necessary assumption, this is likely to underestimate the carbon stock in the reference period and the emission reductions if large trees are conserved in the assessment period. If in the assessment period, instead, such trees were selectively removed, emission reductions could be overestimated.

Forest carbon stocks

Tree biomass was converted using a carbon fraction of 0.47 (IPCC 2006). Plot areas were adjusted for their average slope angle using cos (slope), and carbon stock per hectare was established. The slope correction improves the likelihood that each forest plot contained the same total area as seen on a satellite image. The area-weighted means of forest carbon stocks were calculated parametrically and compared to an empirical bootstrap distribution that re-sampled with replacement 1000 times (Efron 1979, Guan 2003).

In addition to natural variation, the forest carbon stock estimate contains uncertainty from sampling error, measurement error, and the errors inherent in underlying equations and assumptions. Table 1 identifies these sources of uncertainty and the methods applied in this study to reduce uncertainty, emphasizing our focus on sampling error.

Emission reductions

Emission reductions are evaluated by the difference between a business-as-usual (BAU) deforestation scenario and a REDD project scenario. Analysis of LANDSAT TM images from 1986, 2000, 2006, and 2009 by the BERSMP established a deforestation rate of 4%. This estimate was based on differing sensed spectral reflectance that are classified into vegetation types and then ground-truthed (Achard et al. 2001, Andersson et al. 2009). The BAU deforestation scenario in the Bale Mountains was therefore assumed to be a basic linear deforestation rate of 4% of the 2009 forest area in all forest types, as also modeled in the Bale Mountains REDD feasibility studies (UNIQUE 2008, 2010). Future project development would require the acquisition of a broader time scale of satellite imagery in addition to more complex models of forest changes, for example linked with models of the forest transition or to the drivers of deforestation (e.g., Barbier et al. 2010, Estrada and Joseph 2012).

Avoided deforestation under a REDD project is based on project goals, with the effect of PFM on deforestation being subjective rather than based on past experience. While there are findings that community forest management and greater rule-making autonomy at the local level can lead to emissions reductions and greater carbon storage (Chhatre and Agrawal 2009, Skutsch and Ba 2010), this is not universally the case. Experience of the Integrated Forest Management Project of Adaba-Dodola documented by Kubasa and Tadesse (2002) and Tesfaye et al. (2011), also located in the Bale Mountains, for example, suggests that deforestation is occurring more rapidly immediately outside of the PFM area; thus, leakage is being experienced. The estimate of reductions in deforestation, therefore, will need to be revised regularly as the intervention progresses and new information is acquired.

The annual emission reductions generated by the Bale Mountains REDD project is expressed in Eq. 1. Et,i are emission reductions in tons of carbon dioxide (tCO2) in year t, utilizing forest carbon stock estimate Ci (tC/ha) where i can take the value of 1, 2, or 3, representing the three forest carbon stock estimates used to model emission reductions. DBAU is the annual BAU deforestation in hectares in a without-project scenario ; DREDD is the area of deforestation (ha) during the project in year t; and 44/12 is the ratio of the molecular weight of carbon dioxide to that of carbon.
Equation 1(1)

The annual area of deforestation under a REDD project scenario, DREDD, is based on reducing deforestation below the annual BAU scenario of 4% in three stages. In years 1 to 5, DREDD is 3%, in years 6 to 10, DREDD is 2%, and in years 11 to 20, DREDD is 1%. The total emission reductions generated by the project, Eproject, i (tCO2), can be represented by Eq. 2, which sums annual emissions over the 20-year project lifespan.
Equation 2(2)

This emission reductions accounting is a static representation of the Bale Mountains. The methodology inherently assumes a steady state in mature forest, but this assumption is still under debate (Phillips et al. 1998, Houghton 2005, Bonan 2008, Grote et al. 2011). It is also assumed that all carbon in biomass is emitted to the atmosphere at the time of forest loss. This may overestimate emission reductions as harvested wood products such as housing and farm implements are manufactured (Karjalainen et al. 1999, Lim et al. 1999). However, with no consensus on how to account for harvested wood products, this assumption is necessary (Winjum et al. 1998).

Estimating revenues and REDD profits

Potential revenues under a REDD project scenario were calculated using the best- and worst-case emission reductions estimates. Discounted REDD profit is expressed by Eq. 3, where πi is the profit in 2010 US$ over the 20-year lifespan project utilizing forest carbon stock estimates denoted by subscript i, Et,i are the emission reductions generated by the project in year t (tCO2), B is a nontradable risk buffer of emission reductions expressed as a proportion, p is the price per ton of CO2 in US$, r is the registry cost per ton of CO2 in US$, A is the annual operating cost of the project in US$, δ is the discount rate, and K is the up-front costs (US$) of project establishment experienced in project year 1.
Equation 3(3)

The buffer accounts for the risk that emission reductions from forestry carbon projects may not persist over time, termed “nonpermanence,” or may be displaced, termed “leakage.” Permanence concerns arise because forest carbon stocks could deteriorate or be depleted over time due to natural disturbances such as fire, pests, and disease, or anthropogenic disturbances such as political instability (Sedjo and Marland 2003). Leakage concerns arise where emission-generating activities are relocated rather than reduced, leading to fewer, or even no, net emission reductions resulting from REDD project activities (Sohngen and Brown 2004). To deal with these nondelivery risks, a nontradable buffer, or reserve, of emission reductions is commonly set aside as insurance (Peskett et al. 2008). In this study, 40% of emission reductions was set aside for nonpermanence, and a further 25% of emission reductions was set aside for leakage. These buffers are at the higher ranges for project activities, and reflect imminent infrastructure development, a history of forest fire, and potential land tenure disputes and political instability in the Bale Mountains (UNIQUE 2010).

The remaining emission reductions were valued at predicted over-the-counter (OTC) VCM prices. While social costing of carbon would value emission reductions more highly at US$23 per ton of carbon dioxide equivalents (tCO2e) (Tol 2008), the VCM is currently the only trading platform to realize the value of avoided deforestation. The VCM contracted by 47% in 2009, and there was an 11% decline in the price of emission reductions over all sectors following three years of steady growth. Emission reduction prices ranged from US$0.30 to US$111/tCO2e, with an average price of US$6.50/tCO2e (Hamilton et al. 2010). Since 2009, OTC prices have stabilized around US$6/tC02e. The average price for REDD in 2009 was lower than the market average of US$2.9/tCO2e but increased to US$5/tCO2e in 2010, and ranged from US$1 to US$125/tCO2e (Peters-Stanley et al. 2011). There is uncertainty in the price that can be expected for emission reductions from the Bale Mountains. Early interest in the project from buyers indicated that emission reductions could sell for US$3/tCO2e (UNIQUE 2010). If the Bale Mountains REDD project is certified to VCM standards, emissions reductions might receive a price premium; therefore, potential revenues were estimated using both US$3 and US$6.

Listing emission reductions in a public registry increases transparency within the VCM, and the costs of doing so, r, were estimated at $0.10/tCO2e. Annual monitoring, verification, and operational costs of PFM, A, were estimated by forest carbon consultancy UNIQUE and the BERSMP at US$650,000 and subtracted from sales revenues. One-off project establishment costs in year one, K, were similarly estimated at US$3,225,000 (UNIQUE 2010). This cost includes establishment woodlots, 15 PFM units, project design documentation development, and validation to Voluntary Carbon Standards (UNIQUE 2010). While substantial, these cost estimates concur with existing literature on REDD project implementation costs (Cacho et al. 2005, Antinori and Sathaye 2007, Nepstad et al. 2007, WCS 2009). Resultant profits over the full 20-year REDD project lifespan were calculated in 2010 US$ using constant discount rates of 5% and 10% following Grieg-Gran (2006) and the Stern Review (Stern 2007). Because the details of revenue sharing between the various forest stakeholders are yet to be decided, profits are reported pre-tax and with no prior assumptions made about the returns to stakeholders. The uncertainties impacting REDD profits, as well as the method by which these uncertainties are addressed in this study, are summarized in Table 2.


Within 108 forest plots, the dbh of 2698 trees was measured. An average of 35 trees per plot was found, with nondegraded moist forest (59) containing more than dry forest (20). Allometric equations applied to primary field data indicated forest carbon stocks of 289 tC/ha ± 108 (expressed as the 95% confidence interval of the mean) in moist nondegraded forest, followed by moist degraded forest at 199 tC/ha ± 54 and dry degraded forest at 132 tC/ha ± 73 (Fig. 2). Although forest carbon stock distribution was non-normal for all forest types (Shapiro-Wilks for moist nondegraded forest: n = 32, W = 0.77, p < 0.000; moist degraded forest: n = 58, W = 0.76, p < 0.000; dry forest: n = 18, W = 0.68, p < 0.000), a robust bootstrapped distribution that re-sampled with replacement 1000 times gave a very similar result to the normal approximation (Table 3). Nonparametric comparison of carbon stock showed a significant difference between forest types at the 5% level (Kruskall-Wallis, K = 6.942, df = 2105, p = 0.0311).

The standard error of the forest carbon stock estimate was 19% in moist nondegraded forest, 14% in moist degraded, and 28% in dry degraded forest. The sampling error of the primary field data was, therefore, much higher than Chave et al.’s (2004) reported sampling error of 10% of the mean. However, the sample size of 108 plots was past the point where substantial gains in precision are made by increasing sample size (Fig. 3). Increasing the precision of the forest carbon stock estimate to 10% would require data from three times as many, or 347, forest plots. The confidence interval of the primary forest carbon stock estimate was also large, particularly for dry degraded forest, due to the small sample size (n = 18). High variation in above-ground forest carbon stocks due to variation in temperature, precipitation, and soil fertility has been observed in other studies but was not captured here (Houghton 2005). Signs of human disturbance were also observed in a number of plots, ranging from pathways to evidence of cattle grazing, although average canopy cover was greater than 50% in all forest types.

Area-weighted mean forest carbon stock across the forests of the Bale Mountains was 195 tC/ha ± 81. This is consistent with global forest ranges of 20–400 tC/ha reported by Hairiah et al. (2001), but higher than published Ethiopia-wide data of 37 tC/ha and 47 tC/ha (Brown 1997, FAO 2000). It is more comparable to the Africa-wide estimates of Gibbs et al. (2007) of 30–200 tC/ha, and of Lewis et al. (2009), who estimated average forest carbon stocks from permanent plots across Africa at 202 tC/ha. It is also comparable to smaller scale studies. Glenday (2006) found forest carbon stocks of 330 tC/ha in tropical moist forest in Kenya, although this included below-ground carbon stocks, and Munishi et al. (2010) found comparable tropical moist forest in the Eastern Arc Mountains of Tanzania to be in the range of 252–581 tC/ha. As Baccini et al. (2008) note in their mapping of above-ground biomass in Africa, the continent is very diverse, hosting a wide range of ecosystems that explain variation in biomass and carbon content. Comparing primary data forest carbon stock estimates to biome-averaged and IPCC defaults, in both moist and dry forest, the secondary data are within the lower bound of the primary data confidence interval. Biome-averaged data would, however, underestimate the mean moist forest carbon stock of the Bale Mountains by between 47% and 63% and dry forest carbon stock by an average of 56% (Table 4).

Cumulative emission reductions generated over the 20-year project lifespan based on primary data are twice those generated using IPCC data: 180,272 ktCO2 compared to between 71,305 and 89,723 ktCO2 using ecological zone and Africa-specific data, respectively. This result is comparable to the 144% difference between emission reductions using five forest carbon stock estimates in Panamanian forest (Pelletier et al. 2010). Over the forested project area, positive net revenues are generated under all forest carbon stocks. Primary data estimates generate between US$115 and US$445 per hectare, and IPCC ecological zone data, the lower of the two IPCC estimates, return US$21 to US$152 per hectare. The differences between forest carbon stock methods are marked; in the worst-case scenario using secondary data, US$3/tCO2e and a 10% discount rate, the project does not break even until year 6 (Fig. 4). At a conservative market price of US$3/tCO2e and a 10% discount rate, primary data suggest that a REDD project in the Bale Mountains could generated US$48 million compared to US$9 million using IPCC data (Table 5).


Comparing primary data from the Bale Mountains to biome-averaged forest carbon stocks reported by the IPCC reveals discrepancies higher than the 44% for African rain forest reported in GOFC-GOLD (2008). It supports findings that the application of biome-averaged data tends to underestimate forest carbon stocks as compared to local-level estimates (Grassi et al. 2008, Pelletier et al. 2010, Preece et al. 2012). Primary data reveal high uncertainty surrounding the use of mean estimates. The 95% confidence intervals for primary forest carbon stock estimates are, on average, 39% of the forest strata mean. The large uncertainty results in the overlap of the lower confidence interval bounds of primary data with the upper bounds of the interval around the IPCC estimates. These results suggest that diversity of forests is not sufficiently captured by the 20 ecological zones and four climate domains encompassed by the IPCC data. While our focus is on the impact of forest carbon stocks, it should be recognized that uncertainty in other aspects of the forest carbon stock and in emission reductions accounting, such as the stratification of forest area, application of allometric equations, carbon fraction, forest area estimates, and generation of the baseline will serve to further increase and compound uncertainty as would the inclusion of other carbon pools, such as the below-ground biomass of tree roots or soil carbon (Waggoner 2009, Ciais et al. 2011). In particular, forest area change under the project—activity data—will need to be collected, whereas here it is based on projected deforestation goals of the PFM project. The method used to gather and analyze this activity data will also impact the error of the emission reductions estimate (Achard et al. 2007, Duveiller et al. 2008). Further research is required to understand if additional sources of uncertainties act to reduce or increase estimates of forest carbon stocks, as well as to consider how emission reductions accounting is carried out for the suite of REDD activities that go beyond avoided deforestation.

The discrepancy between carbon stock estimates leads to more than a two-fold difference in emission reductions from a REDD project in the Bale Mountains, a substantial amount in potential profits. This highlights research needs for forest carbon stock methods using local data despite the fact that emissions accounting using biome-averaged data can be undertaken immediately for low or no cost, making them an attractive option. We demonstrate a financial incentive for investing in the capacity to gather primary data. With the popularity of REDD partially dependent on the transfer of finance from developed to developing countries, more complex accounting can also ensure that rewards for reducing deforestation and degradation are of appropriate scale. However, with costs of reducing uncertainty rising as methods become more data intensive, trade-offs may emerge. The costs of increasing the statistical power of forest carbon stock estimates, for example, may be greater than the benefits of improved estimates given diminishing returns in sampling effort. Tools such as sensitivity analysis could be employed to identify components with the most impact on total uncertainty which can then be prioritized (Elston 1992).

Financial analysis is commonplace to guide decisions to implement REDD projects. If estimated revenues are insufficient to meet cost demands of REDD, then other tools to fund forest conservation might be considered (e.g., Morse et al. 2009, Fisher et al. 2011). Conversely, climate change mitigation potential may be lost where emission reductions are more substantial than a feasibility assessment would indicate. All scenarios of the Bale Mountains REDD project predicted net positive profits at year 20, despite underlying differences in forest carbon stocks and VCM prices. Low returns to investment and a long return period, however, may not be sufficient to justify investment in the high up-front costs of REDD. Our financial calculation does not include taxes to federal and regional government or consider the share of payments between numerous forest stakeholders. These details are likely to make the cost-to-benefit ratio even less favorable. Given that finance for forest conservation in the Bale Mountains is low and largely donor funded, however, even small net positive revenues may be a sufficient argument for implementing REDD. As noted in Potapov et al. (2008), forests are important for a variety of purposes. The Bale Mountains is an area of high biodiversity value that supports numerous livelihoods and provides water to many downstream users; thus, other ecosystem services will be generated from avoided deforestation, which might factor into decision-making (BMNP 2007). Using PFM to generate REDD may also shift forest resource use onto a sustainable pathway, supporting livelihoods and contributing towards clarifying property and forest-use rights for local peoples. Decisions on whether to implement REDD may therefore not rely completely on financial feasibility analyses. This decoupling of REDD policy decisions and cost-benefit analysis is evidenced by cases where the costs of REDD project and policy development are being absorbed by intermediaries or met through donor finance (Watson and Nakhooda 2012).

Many national forest inventories in developing countries are not comprehensive, and limited resources exist for new field measurement (DeFries et al. 2006). The implications of the discrepancy in forest carbon stocks makes the case for earmarking existing REDD finance flows to build the capacity of developing countries to prepare for a future UNFCCC mechanism, for reducing uncertainty and improving national forest inventories through long-term institutional backing and resources. Not only for forest carbon stocks, support could ensure that developing country governments benefit from advances in satellite imagery and improved understanding of emission reductions leakage, which may be more appropriate at a national rather than project level. Improving data on forest monitoring is important regardless of the ultimate financing mechanism for REDD and will serve to reduce the distance between broad averages and spatially limited estimates (Grainger 2010). While we have provided a static assessment of forest carbon stocks, it is also worth noting that a functioning REDD project will require performance to be assessed on an ongoing basis. Ongoing assessments will reduce uncertainties in forest carbon stocks, and when applied to community forestry it can also capture forest carbon stock changes resulting from degradation in addition to deforestation (Skutsch et al. 2011). As implementing REDD through PFM gains prominence, particularly in East Africa (Klooster and Masera 2000, Murdiyarso and Skutsch 2006, Agrawal and Angelsen 2009, Hayes and Persha 2010, Mustalahti et al. 2012), earmarked finance might also be directed to broadening research into linking community forestry with tropical forest science and community-level forest monitoring (Michon et al. 2007, Somanathan et al. 2009, Skutsch and Ba 2010).

We emphasize the financial implications of uncertainty in emission reductions accounting for REDD projects. When biome-averaged carbon stock estimates are compared with estimates established from direct tree measurements, the financial discrepancy is sizeable. If such discrepancies become commonplace, they could call into question the environmental integrity of REDD. Uncertainty may also generate unrealistic expectations of profits that erode the credibility and potential of the mechanism. Delivering REDD has proven more complicated than many initially thought (Watson and Nakhooda 2012), and these findings demonstrate the need to improve the quantification of uncertainty in forest carbon stocks, reduce uncertainty where possible, and communicate uncertainty in a way that can be utilized in policy decisions. Reducing reliance on the conservativeness principle could aid this shift. While conservativeness will remain important to ensure emission reductions are not underestimated in the reference period and overestimated in the assessment period, it should not preclude efforts to reduce forest carbon stock uncertainties. Dealing with decision-making under uncertainty is not novel in climate change policy (Webster et al. 2002), and presently, countries are encouraged but not obliged to include uncertainty estimates in their national communications to the UNFCCC (UNFCCC 2002). While highly uncertain GHG accounting might be acceptable for national communications, it is insufficient for a performance-based incentive mechanism like REDD. Although additional costs will be incurred to reduce uncertainty, and trade-offs between factors in the accounting process may be introduced, the financial incentives for improved emission reductions accounting are clear.


Responses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a response, follow this link. To read responses already accepted, follow this link.


This study was made possible by the Economic and Social Research Council, the British Embassy in Ethiopia, and the Frankfurt Zoological Society. Gratitude is extended to the Bale Mountains National Park, the Darwin Initiative (Biodiversity Monitoring in Forest Ecosystems in Bale Mountains National Park, Ethiopia), and the Bale Eco-Region Sustainable Management Program for access to a wealth of information as well as logistical support. E. J. Milner-Gulland acknowledges a Royal Society Wolfson Research Merit Award.


Achard, F., R. DeFries, H. Eva, M. Hansen, P. Mayaux, and H. J. Stibig. 2007. Pan-tropical monitoring of deforestation. Environmental Research Letters 2.

Achard, F., H. Eva, and P. Mayaux. 2001. Tropical forest mapping from coarse spatial resolution satellite data: production and accuracy assessment issues. International Journal of Remote Sensing 22:2741–2762.

Achard, F., H. D. Eva, P. Mayaux, H. J. Stibig, and A. Belward. 2004. Improved estimates of net carbon emissions from land cover change in the tropics for the 1990s. Global Biogeochemical Cycles 18:B2008.

Agrawal, A., and A. Angelsen. 2009. Using community forest management to achieve REDD+ goals. Pages 201–212 in A. Angelsen, M. Brockhaus, M. Kanninen, E. Sills, W. D. Sunderlin, and S. Wertz-Kanounnikoff, editors. Realising REDD+: national strategy and policy options. Center for International Forestry Research (CIFOR), Bogor, Indonesia.

Andersson, K., T. Evans, and K. Richards. 2009. National forest carbon inventories: policy needs and assessment capacity. Climatic Change 93:69–101.

Angelsen, A. 2008. Moving ahead with REDD: issues, options and implications. Center for International Forestry Research (CIFOR), Bogor, Indonesia.

Angelsen, A., and D. McNeill. 2012. The evolution of REDD+. Pages 31–48 in A. Angelsen, M. Brockhaus, W. D. Sunderlin, and L. Verchot, editors. Analysing REDD+: challenges and choices. Center for International Forestry Research (CIFOR), Bogor, Indonesia.

Antinori, C., and J. Sathaye. 2007. Assessing transaction costs of project-based greenhouse gas emissions trading. Formal Report LBNL-57315, Lawrence Berkeley National Laboratory, Berkeley, California, USA.

Baccini, A., N. Larporte, S. Goetz, M. Sun, and H. Dong. 2008. A first map of tropical Africa's above-ground biomass derived from satellite imagery. Environmental Research Letters 3:045011.

Baker, D. J., G. Richards, A. Grainger, P. Gonzalez, S. Brown, R. DeFries, A. Held, J. Kellndorfer, P. Ndunda, D. Ojima, P.-E. Skrovseth, C. Souza, Jr., and F. Stolle. 2010. Achieving forest carbon information with higher certainty: a five-part plan. Environmental Science & Policy 13:249–260.

Bale Eco-Region Sustainable Management Programme (BERSMP). 2006. Bale Eco-Region Sustainable Management Programme project document. FARM-Africa/SOS Sahel, Addis Ababa, Ethiopia.

Bale Mountains National Park (BMNP). 2007. Bale Mountains National Park: General Management Plan 2007–2017. BMNP with Frankfurt Zoological Society, Addis Ababa, Ethiopia.

Barbier, E. B., J. C. Burgess, and A. Grainger. 2010. The forest transition: towards a more comprehensive theoretical framework. Land Use Policy 27:98–107.

Bonan, G. B. 2008. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320:1444–1449.

Bond, I., M. Grieg-Gran, S. Wertz-Kanounnikoff, P. Hazlewood, S. Wunder, and A. Angelsen. 2009. Incentives to sustain forest ecosystem services: a review and lessons for REDD. International Institute for Environment and Development, London, UK, with CIFOR, Bogor, Indonesia, and World Resources Institute, Washington D.C., USA.

Böttcher, H., K. Eisbrenner, S. Fritz, G. Kindermann, F. Kraxner, I. McCallum, and M. Obersteiner. 2009. An assessment of monitoring requirements and costs of Reduced Emissions from Deforestation and Degradation. Carbon Balance and Management

Boucher, D. 2008. What REDD can do: the economics and development of reducing emissions from deforestation and forest degradation. Tropical Forest and Climate Initiative, Union of Concerned Scientists, Washington, D.C., USA.

Bradford, J. B., P. Weishampel, M.-L. Smith, R. Kolka, R. A. Birdsey, S. V. Ollinger, and M. G. Ryan. 2010. Carbon pools and fluxes in small temperate forest landscapes: variability and implications for sampling design. Forest Ecology and Management 259:1245–1254.

Brown, S. 1997. Estimating biomass and biomass change of tropical forests: a primer. FAO, Rome, Italy.

Brown, S. 2002. Measuring, monitoring, and verification of carbon benefits for forest-based projects. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 360:1669–1683.

Brown, S., and G. Gaston. 1995. Use of forest inventories and geographic information systems to estimate biomass density of tropical forests: application to tropical Africa. Environmental Monitoring and Assessment 38:157–168.

Brown, S., A. J. R. Gillespie, and A. E. Lugo. 1989. Biomass estimation methods for tropical forests with applications to forest inventory data. Forest Science 35:881–902.

Brown, S., and A. E. Lugo. 1992. Aboveground biomass estimates for tropical moist forests of the Brazilian Amazon. lntereieneia 17:8–18.

Bucki, M., D. Cuypers, P. Mayaux, F. Achard, C. Estreguil, and G. Grassi. 2012. Assessing REDD+ performance of countries with low monitoring capacities: the matrix approach. Environmental Research Letters 7:014031.

Cacho, O. J., G. R. Marshall, and M. Milne. 2005. Transaction and abatement costs of carbon-sink project in developing countries. Environment and Development Economics 10:597–614.

Chave, J., C. Andalo, S. Brown, M. A. Cairns, J. Q. Chambers, D. Eamus, H. Folster, F. Fromard, N. Higuchi, T. Kira, J. P. Lescure, B. W. Nelson, H. Ogawa, H. Puig, B. Riera, and T. Yamakura. 2005. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145:87–99.

Chave, J., R. Condit, A. Salomon, A. Hernandez, S. Lao, and R. Perez. 2004. Error propagation and scaling for tropical forest biomass estimates. Philosophical Transactions of the Royal Society B: Biological Sciences 359:409–420.

Chhatre, A., and A. Agrawal. 2009. Trade-offs and synergies between carbon storage and livelihood benefits from forest commons. Proceedings of the National Academy of Sciences 106:17667–17670.

Chomitz, K. M. 2007. At loggerheads? Agricultural expansion, poverty reduction, and environment in the tropical forests. World Bank, Washington, D.C., USA.

Ciais, P., A. Bombelli, M. Williams, S. L. Piao, J. Chave, C. M. Ryan, M. Henry, P. Brender, and R. Valentini. 2011. The carbon balance of Africa: synthesis of recent research studies. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 369:2038–2057.

Clark, D. B., and D. A. Clark. 2000. Landscape-scale variation in forest structure and biomass in a tropical rain forest. Forest Ecology and Management 137:185–198.

DeFries, R., F. Achard, S. Brown, M. Herold, D. Murdiyarso, B. Schlamadinger, and C. de Souza, Jr. 2006. Reducing greenhouse gas emissions from deforestation in developing countries: considerations for monitoring and measuring. Report of the Global Terrestrial Observing System (GTOS) Number 46, GOFC-GOLD.

DeFries, R., F. Achard, S. Brown, M. Herold, D. Murdiyarso, B. Schlamadinger, and C. de Souza, Jr. 2007. Earth observations for estimating greenhouse gas emissions from deforestation in developing countries. Environmental Science & Policy 10:385–394.

Diaz, D., K. Hamilton, and E. Johnson. 2011. State of the forest carbon markets 2011: from canopy to currency. Ecosystem Marketplace, Washington, D.C., USA.

Djomo, A. N., A. Ibrahima, J. Saborowski, and G. Gravenhorst. 2010. Allometric equations for biomass estimations in Cameroon and pan moist tropical equations including biomass data from Africa. Forest Ecology and Management 260:1873–1885.

Dupuy, J. 2009. GIS analyst. Bale Eco-Region Sustainable Management Programme.

Duveiller, G., P. Defourny, B. Desclee, and P. Mayaux. 2008. Deforestation in Central Africa: estimates at regional, national and landscape levels by advanced processing of systematically-distributed Landsat extracts. Remote Sensing of Environment 112:1969–1981.

Efron, B. 1979. Bootstrap methods: another look at the jackknife. Annals of Statistics 7:1–26.

Elston, D. A. 1992. Sensitivity analysis in the presence of correlated parameter estimates. Ecological Modelling 64:11–22.

Estrada, M. 2011. Standards and methods available for estimating project level REDD+ carbon benefits: reference guide for project developers. Center for International Forestry Research (CIFOR), Bogor, Indonesia.

Estrada, M., and S. Joseph. 2012. Baselines and monitoring in local REDD+ projects. Pages 247–260 in A. Angelsen, M. Brockhaus, W. Sunderlin, and L. Verchot, editors. Analysing REDD+: challenges and choices. Center for International Forestry Research (CIFOR), Bogor, Indonesia.

Fisher, B., S. L. Lewis, N. D. Burgess, R. E. Malimbwi, P. K. Munishi, R. D. Swetnam, R. Kerry Turner, S. Willcock, and A. Balmford. 2011. Implementation and opportunity costs of reducing deforestation and forest degradation in Tanzania. Nature Climate Change 1:161–164.

Food and Agriculture Organization (FAO). 2000. Global forest resources assessment 2000. FAO, Rome, Italy.

Food and Agriculture Organization (FAO). 2003. Wood volume and woody biomass: review of FRA 2000 estimates. FAO, Rome, Italy.

Food and Agriculture Organization (FAO). 2010. Global forest resources assessment 2010. FAO, Rome, Italy.

Forest Carbon Partnership Facility. 2011. REDD readiness preparation proposal for Federal Democratic Republic of Ethiopia (R-PP). REDD Technical Working Group, Addis Ababa, Ethiopia.

Forests Philanthropy Action Network (FPAN). 2010. Protecting and restoring forest carbon in tropical Africa: a guide for donors and funders. Forests Philanthropy Action Network.

Gibbs, H. K., S. Brown, J. O. Niles, and J. A. Foley. 2007. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters 2:045023.

Glenday, J. 2006. Carbon storage and emissions offset potential in an East African tropical rainforest. Forest Ecology and Management 235:72–83.

Goetz, S., A. Baccini, N. Laporte, T. Johns, W. Walker, J. Kellndorfer, R. A. Houghton, and M. Sun. 2009. Mapping and monitoring carbon stocks with satellite observations: a comparison of methods. Carbon Balance and Management 4:2.

GOFC-GOLD. 2008. Reducing greenhouse gas emissions from deforestation and degradation in developing countries: a sourcebook of methods and procedures for monitoring, measuring and reporting. GOFC-GOLD Project Office, Natural Resources Canada, Alberta, Canada.

Grainger, A. 2008. Difficulties in tracking the long-term global trend in tropical forest area. Proceedings of the National Academy of Sciences 105:818–823.

Grainger, A. 2010. Uncertainty in the construction of global knowledge of tropical forests. Progress in Physical Geography 34:811–844.

Grassi, G., S. Monni, S. Federici, F. Achard, and D. Mollicone. 2008. Applying the conservativeness principle to REDD to deal with the uncertainties of the estimates. Environmental Research Letters 3:035005.

Greenhalgh, S., F. Daviet, and E. Weninger. 2006. The land use, land-use change, and forestry guidance for GHG project accounting. World Resources Institute, Washington, D.C., USA.

Grieg-Gran, M. 2006. The cost of avoiding deforestation. International Institute for Environment and Development, London, UK.

Griscom, B., D. Shoch, B. Stanley, R. Cortez, and N. Virgilio. 2009. Sensitivity of amounts and distribution of tropical forest carbon credits depending on baseline rules. Environmental Science & Policy 12:897–911.

Groom, B., C. Hepburn, P. Koundouri, and D. Pearce. 2005. Declining discount rates: the long and the short of it. Environmental and Resource Economics 32:445–493.

Grote, R., R. Kiese, T. Grünwald, J.-M. Ourcival, and A. Granier. 2011. Modelling forest carbon balances considering tree mortality and removal. Agricultural and Forest Meteorology 151:179–190.

Guan, W. 2003. From the help desk: bootstrapped standard errors. Stata Journal 3:71–80.

Hairiah, K., S. M. Sitompul, M. van Noordwijk, and C. Palm. 2001. Methods for sampling carbon stocks above and below ground. International Center for Research in Agroforestry (ICRAF), Bogor, Indonesia.

Hamilton, K., M. Sjardin, M. Peters-Stanley, and T. Marcello. 2010. Building bridges: state of the voluntary carbon markets 2010. Ecosystem Marketplace and Bloomberg New Energy Finance, London, UK.

Hayes, T., and L. Persha. 2010. Nesting local forestry initiatives: revisiting community forest management in a REDD+ world. Forest Policy and Economics 12:545–553.

Henry, M., N. Picard, C. Trotta, M. R, R. Valentini, M. Bernoux, and L. Saint-André. 2011. Estimating tree biomass of Sub-Saharan African forests: a review of available allometric equations. Silva Fennica 45:477–569.

Hepburn, C. J., and P. Koundouri. 2007. Recent advances in discounting: implications for forest economics. Journal of Forest Economics 13:169–189.

Houghton, R. A. 2005. Aboveground forest biomass and the global carbon balance. Global Change Biology 11:945–958.

Houghton, R. A., K. T. Lawrence, J. L. Hackler, and S. Brown. 2001. The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Global Change Biology 7:731–746.

Huettner, M., R. Leemans, K. Kok, and J. Ebeling. 2009. A comparison of baseline methodologies for ‘Reducing Emissions from Deforestation and Degradation’. Carbon Balance and Management 4:doi:10.1186/1750-0680-1184-1184.

Intergovernmental Panel on Climate Change (IPCC). 2003. Good practice guidance for land use, land-use change and forestry. J. Penman, M. Gytarsky, T. Hiraishi, T. Krug, D. Kruger, R. Pipatti, L. Buendia, K. Miwa, T. Ngara, K, Tanabe, and F. Wagner, editors. Institute for Global Environmental Strategies, Hayama, Kanagawa, Japan.

Intergovernmental Panel on Climate Change (IPCC). 2006. Guidelines for national greenhouse gas inventories. National Greenhouse Gas Inventories Programme, IGES, Japan.

Karjalainen, T., A. Pussinen, S. Kellomäki, and R. Mäkipää. 1999. Scenarios for the carbon balance of Finnish forests and wood products. Environmental Science & Policy 2:165–175.

Keller, M., M. Palace, and G. Hurtt. 2001. Biomass estimation in the Tapajos National Forest, Brazil—examination of sampling and allometric uncertainties. Forest Ecology and Management 154:371–382.

Kerr, S., J. Hendy, L. Shuguang, and A. S. P. Pfaff. 2004. Tropical forest protection, uncertainty, and the environmental integrity of carbon mitigation policies. Motu Working Paper 04-03, Motu Economic and Public Policy Research, Wellington, New Zealand.

Ketterings, Q. M., R. Coe, M. Van Noordwijk, Y. Ambagau, and C. A. Palm. 2001. Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. Forest Ecology and Management 146:199–209.

Klooster, D., and O. Masera. 2000. Community forest management in Mexico: carbon mitigation and biodiversity conservation through rural development. Global Environmental Change 10:259–272.

Kubsa, A., and T. Tadesse. 2002. Granting exclusive user rights to the forest dwellers in the state-owned forest: the WAJIB approach in Ethiopia. Pages 89–98 in Proceedings of the Second International Workshop on Participatory Forestry in Africa, Arusha, Tanzania, 18–22 February 2002. FAO, Rome, Italy.

Larocque, G. R., J. S. Bhatti, R. Boutin, and O. Chertov. 2008. Uncertainty analysis in carbon cycle models of forest ecosystems: research needs and development of a theoretical framework to estimate error propagation. Ecological Modelling 219:400–412.

Lewis, S. L., G. Lopez-Gonzalez, B. Sonke, K. Affum-Baffoe, T. R. Baker, L. O. Ojo, O. L. Phillips, J. M. Reitsma, L. White, J. A. Comiskey, M. N. K. Djuikouo, C. E. N. Ewango, T. R. Feldpausch, A. C. Hamilton, M. Gloor, T. Hart, A. Hladik, J. Llyod, J. C. Lovett, J. -R. Makana, Y. Malhi, F. M. Mbago, H. J. Ndangalasi, J. Peacock, K. S. H. Peh, D. Sheil, T. Sunderland, M. D. Swaine, J. Taplin, D. Taylor, S. C. Thomas, R. Votere, and H. Woll. 2009. Increasing carbon storage in intact African tropical forests. Nature 457:1003–1006.

Lim, B., S. Brown, and B. Schlamadinger. 1999. Carbon accounting for forest harvesting and wood products: review and evaluation of different approaches. Environmental Science & Policy 2:207–216.

MacDicken, K. 1997. A guide to monitoring carbon storage in forestry and agroforestry projects. Winrock International, Arlington, Virginia, USA.

Marshall, A. R., S. Willcock, P. J. Platts, J. C. Lovett, A. Balmford, N. D. Burgess, J. E. Latham, P. K. T. Munishi, R. Salter, D. D. Shirima, and S. L. Lewis. 2012. Measuring and modelling above-ground carbon and tree allometry along a tropical elevation gradient. Biological Conservation 154:20–33.

Mayaux, P., P. Holmgren, F. Achard, H. Eva, H.-J. Stibig, and A. Branthomme. 2005. Tropical forest cover change in the 1990s and options for future monitoring. Philosophical Transactions of the Royal Society B: Biological Sciences 360:373–384.

Michon, G., H. De Foresta, P. Levang, and F. Verdeaux. 2007. Domestic forests: a new paradigm for integrating local communities’ forestry into tropical forest science. Ecology and Society 12(2):1. [online] URL:

Mollicone, D., A. Freibauer, E. D. Schulze, S. Braatz, G. Grassi, and S. Federici. 2007. Elements for the expected mechanisms on ‘reduced emissions from deforestation and degradation, REDD’ under UNFCCC. Environmental Research Letters 2:045024.

Monni, S., M. Peltoniemi, T. Palosuo, A. Lehtonen, R. Mäkipää, and I. Savolainen. 2007. Uncertainty of forest carbon stock changes—implications to the total uncertainty of GHG inventory of Finland. Climate Change 81:391–413.

Morse, W. C., J. L. Schedlbauer, S. E. Sesnie, B. Finegan, C. A. Harvey, S. J. Hollenhorst, K. L. Kavanagh, D. Stoian, and J. D. Wulfhorst. 2009. Consequences of environmental service payments for forest retention and recruitment in a Costa Rican biological corridor. Ecology and Society 14(1):23. [online] URL:

Munishi, P. K., S. Mringi, D. D. Shirima, and S. K. Linda. 2010. The role of the Miombo Woodlands of the Southern Highlands of Tanzania as carbon sinks. Journal of Ecology and the Natural Environment 2:261–269.

Murdiyarso, D., and M. Skutsch. 2006. Community forest management as a carbon mitigation option: case studies. Center for International Forestry Research (CIFOR), Bogor, Indonesia.

Mustalahti, I., A. Bolin, E. Boyd, and J. Paavola. 2012. Can REDD+ reconcile local priorities and needs with global mitigation benefits? Lessons from Angai Forest, Tanzania. Ecology and Society 17(1):16.

Nagendra, H., and E. Ostrom. 2011. The challenge of forest diagnostics. Ecology and Society 16(2):20. [online]

Nepstad, D., B. Soares-Filho, F. Merry, P. Moutinho, H. O. Rodrigues, M. Bowman, S. Schwartzman, O. Almeida, and S. Rivero. 2007. The costs and benefits of reducing carbon emissions from deforestation and forest degradation in the Brazilian Amazon. Woods Hole Research Center, Falmouth, Massachusetts, USA.

Olander, L. P., H. K. Gibbs, M. Steininger, J. Swenson, and B. C. Murray. 2008. Reference scenarios for deforestation and forest degradation in support of REDD: a review of data and methods. Environmental Research Letters 3:025011.

Paquette, A., J. Hawryshyn, A. Vyta Senikas, and C. Potvin. 2009. Enrichment planting in secondary forests: a promising clean development mechanism to increase terrestrial carbon sinks. Ecology and Society 14(1):31. [online] URL:

Parker, C., A. Mitchell, M. Trivedi, and N. Mardas. 2008. The little REDD book: a guide to governmental and non-governmental proposals to reducing emissions from deforestation and degradation. Global Canopy Programme, Oxford, UK.

Pearce, D. W., B. Groom, C. Hepburn, and P. Koundouri. 2003. Valuing the future: recent advances in social discounting. World Economics 4:121–141.

Pearson, T., S. Walker, and S. Brown. 2005. Sourcebook for land use, land-use change and forestry projects. Winrock International and the Bio Carbon Fund of the World Bank.

Pelletier, J., K. R. Kirby, and C. Potvin. 2010. Significance of carbon stock uncertainties on emission reductions from deforestation and forest degradation in developing countries. Forest Policy and Economics 24:3–11.

Peskett, L., D. Huberman, E. Bowen-Jones, G. Edwards, and J. Brown. 2008. Making REDD work for the poor. Overseas Development Institute and International Union for Conservation of Nature, London, UK.

Peters-Stanley, M., K. Hamilton, T. Marcello, and M. Sjardin. 2011. Back to the future: state of the voluntary carbon markets 2011. Ecosystem Marketplace and Bloomberg New Energy Finance, London, UK.

Phillips, O. L., Y. Malhi, N. Higuchi, W. F. Laurance, P. V. Núñez, R. M. Vásquez, S. G. Laurance, L. V. Ferreira, M. Stern, S. Brown, and J. Grace. 1998. Changes in the carbon balance of tropical forests: evidence from long-term plots. Science 282:439–442.

Potapov, P., A. Yaroshenko, S. Turubanova, M. Dubinin, L. Laestadius, C. Thies, D. Aksenov, A. Egorov, Y. Yesipova, I. Glushkov, M. Karpachevskiy, A. Kostikova, A. Manisha, E. Tsybikova, and I. Zhuravleva. 2008. Mapping the world’s intact forest landscapes by remote sensing. Ecology and Society 13(2):51. [online] URL:

Preece, N. D., G. M. Crowley, M. J. Lawes, and P. van Oosterzee. 2012. Comparing above-ground biomass among forest types in the wet tropics: Small stems and plantation types matter in carbon accounting. Forest Ecology and Management 264:228–237.

Ramankutty, N., H. K. Gibbs, F. Achard, R. Defries, J. A. Foley, and R. A. Houghton. 2007. Challenges to estimating carbon emissions from tropical deforestation. Global Change Biology 13:51–66.

Romijn, E., M. Herold, L. Kooistra, D. Murdiyarso, and L. Verchot. 2012. Assessing capacities of non-Annex I countries for national forest monitoring in the context of REDD+. Environmental Science & Policy 19–20:33–48.

Saatchi, S. S., N. L. Harris, S. Brown, M. Lefsky, E. T. A. Mitchard, W. Salas, B. R. Zutta, W. Buermann, S. L. Lewis, S. Hagen, S. Petrova, L. White, M. Silman, and A. Morel. 2011. Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences 108:9899–9904.

Sedjo, R., and G. Marland. 2003. Inter-trading permanent emissions credits and rented temporary carbon emissions offsets: some issues and alternatives. Climate Policy 3:435–444.

Shackleton, C. M., and R. J. Scholes. 2011. Above ground woody community attributes, biomass and carbon stocks along a rainfall gradient in the savannas of the central lowveld, South Africa. South African Journal of Botany 77:184–192.

Shoch, D., J. Eaton, and S. Settelmyer. 2011. Project developer’s guidebook to VCS REDD methodologies. Version 1.0. Conservation International.

Skutsch, M., and L. Ba. 2010. Crediting carbon in dry forests: the potential for community forest management in West Africa. Forest Policy and Economics 12:264–270.

Skutsch, M., A. Torres, T. Mwampamba, A. Ghilardi, and M. Herold. 2011. Dealing with locally-driven degradation: a quick start option under REDD+. Carbon Balance and Management 6:16.

Smith, J. E., and L. S. Heath. 2001. Identifying influences on model uncertainty: an application using a forest carbon budget model. Environmental Management 27:253–267.

Sohngen, B., and S. Brown. 2004. Measuring leakage from carbon projects in open economies: a stop timber harvesting project in Bolivia as a case study. Canadian Journal of Forestry Research 34:829–839.

Somanathan, E., R. Prabhakar, and B. S. Mehta. 2009. Decentralization for cost-effective conservation. Proceedings of the National Academy of Sciences 106:4143–4147.

Stern, N. 2007. The economics of climate change: the Stern Review. Cambridge University Press, Cambridge, UK.

Tesfaye, Y., A. Roos, B. M. Campbell, and F. Bohlin. 2011. Livelihood strategies and the role of forest income in participatory-managed forests of Dodola area in the Bale highlands, southern Ethiopia. Forest Policy and Economics 13:258–265.

Teshome, E., D. Randall, and A. A. Kinahan. 2011. The changing face of the Bale Mountains National Park over 32 years: a study on land cover change. Walia Journal of the Ethiopian Wildlife and Natural History Society 118–130.

Tol, R. S. J. 2008. The social cost of carbon: trends, outliers and catastrophes. Economics: the Open-Access, Open-Assessment E-Jounal 2:1–24.

UNIQUE Forestry Consultants (UNIQUE). 2008. Sustainable financing mechanisms for the BESMP. Part II: carbon finance opportunities. UNIQUE Forestry Consultants, Freiburg, Germany.

UNIQUE Forestry Consultants (UNIQUE). 2010. Bale Mountain REDD project: protecting highland forests in Ethiopia. Project marketing document. UNIQUE Forestry Consultants, Freiburg, Germany.

United Nations Development Programme (UNDP). 2009. Forestry carbon accounting, overview and principles. UNDP-UNEP CDM Capacity Development Project for Eastern & Southern Africa, Addis Ababa, Ethiopia.

United Nations Framework Convention on Climate Change (UNFCCC). 2002. Report of the Conference of the Parties on its seventh session, held at Marrakesh from 29 October to 10 November 2001. Part two: action taken by the Conference of the Parties. FCCC/CP/2001/13 Addendum. UNFCCC Secretariat.

United Nations Framework Convention on Climate Change (UNFCCC). 2009. Cost of implementing methodologies and monitoring systems relating to estimates of emissions from deforestation and forest degradation, the assessment of carbon stocks and greenhouse gas emissions from changes in forest cover, and the enhancement of forest carbon stocks. Technical Paper FCCC/TP/2009/1.

van Breugel, M., J. Ransijn, D. Craven, F. Bongers, and J. S. Hall. 2011. Estimating carbon stock in secondary forests: decisions and uncertainties associated with allometric biomass models. Forest Ecology and Management 262:1648–1657.

Waggoner, P. E. 2009. Forest inventories: discrepancies and uncertainties. Resources for the Future Discussion Paper 09-29, Washington, D.C., USA.

Watson, C., and S. Nakhooda. 2012. Financing readiness: insights from the Amazon Fund and Congo Basin Forest Fund’s efforts to reduce emissions from deforestation and degradation. ODI and HBF, London, UK.

Webster, M. D., M. Babiker, M. Mayer, J. M. Reilly, J. Harnisch, R. Hyman, M. C. Sarofim, and C. Wang. 2002. Uncertainty in emissions projections for climate models. Atmospheric Environment 36:3659–3670.

Weitzman, M. 1998. Why the far distant future should be discounted at its lowest possible rate. Journal of Environmental Economics and Management 36:201–208.

Westlake, D. F. 1966. The biomass and productivity of Glyceria maxima. I. Seasonal changes in biomass. Journal of Ecology 54:745–753.

Wildlife Conservation Society (WCS). 2009. REDD project development guide. Wildlife Conservation Society.

Winjum, J. K., S. Brown, and B. Schlamadinger. 1998. Forest harvests and wood products: sources and sinks of atmospheric carbon dioxide. Forest Science 44:272–284.

Yimer, F., S. Ledin, and A. Abdelkadir. 2006. Soil organic carbon and total nitrogen stocks as affected by topographic aspect and vegetation in the Bale Mountains, Ethiopia. Geoderma 135:335–344.

Address of Correspondent:
Charlene Watson
Department of Geography and the Environment, London School of Economics and Political Science, Houghton Street, London, WC2A 2AE, UK
Jump to top
Table1  | Table2  | Table3  | Table4  | Table5  | Figure1  | Figure2  | Figure3  | Figure4