progress toward a consensus on carbon emissions...

AProgress toward a Consensus on Carbon Emissions from Tropical Deforestation

PROGRESS TOWARD A CONSENSUS ON CARBON EMISSIONS FROM TROPICAL DEFORESTATION

Winrock International Team

Dr. Nancy Harris, Senior Carbon and Land Use Specialist, Ecosystem Services Unit, Winrock International

Dr. Sandra Brown, Director and Chief Scientist, Ecosystem Services Unit, Winrock International

Dr. Stephen C. Hagen, Senior Research Scientist, Applied Geosolutions, LLC

Woods Hole Research Center Team

Dr. Alessandro Baccini, Assistant Scientist, Woods Hole Research Center

Dr. Richard Houghton, Senior Scientist, Woods Hole Research Center

Woods Hole Research CenterScience, Education, and Policy for A Healthy Planet

POLICY BRIEF

B

Disclaimer: Any views expressed in this policy brief are those of the authors. They do not necessarily represent the views of the authors’ institutions, Meridian Institute, or the financial sponsors of this policy brief.

ISBN: 978-0-615-72677-9

Date of Publication November 2012

CONTENTSExecutive Summary .......................................... 1Acknowledgments ............................................ 2Acronyms ......................................................... 31. Introduction .................................................. 42. Main Differences .......................................... 4

Emissions from Deforestation vs. from Land-Use Change ................................................. 4

Different Spatial Scales ....................................... 5

Different Carbon Stock Maps ..............................5

Different Carbon Emission Models ......................6

3. Toward Consensus ....................................... 64. Conclusions and Implications for REDD+ Monitoring ...................................... 85. Agreement on a Pantropical Benchmark ....................................................... 8References ....................................................... 9

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1Progress toward a Consensus on Carbon Emissions from Tropical Deforestation

EXECUTIVE SUMMARYDuring 2012, our research groups – Woods Hole Research Center (WHRC) and Winrock International (Winrock) – published two estimates of carbon emissions from tropical deforestation that, upon first review, seemed to differ widely.1,2 We have closely examined the differences in these estimates and conclude that there is more agreement than we realized previously. The largest differences relate primarily to definitional issues and to the differing scopes of the two studies. Based on our examination and analyses of the data and methodologies, both groups agree that, when accounting for the same carbon pools and for the same time frame of 2000–2005, emissions from gross deforestation in tropical regions contributed 3.0 Gt CO2 yr-1 (0.8 Pg C yr-1) to the atmosphere. This value does not fully account for emissions from tropical forest land-use change because it excludes emissions from mineral soils and peat as well as emissions from forest degradation activities, which could account for another 2.3 Gt CO2 yr-1 (0.6 Pg C yr-1). Uncertainty in deforestation emissions, as reported by the Winrock team, is ± 1.1 Gt CO2 yr-1 (0.3 Pg C yr-1), but the uncertainty in the other emission sources has not been estimated.

Although our consensus on deforestation emissions indicates convergence despite differing methods and data, our estimates still differ with respect to where these emissions occur. Both groups agree that the best approach for accurately quantifying carbon emissions from land-use change is to spatially align activity data and emission factors. The consensus on deforestation emissions at the pantropical scale provides an unbiased historic benchmark against which current and future emissions can be compared.

1 Baccini et al. (2012). 2 Harris et al. (2012).

2

ACKNOWLEDGMENTSWe gratefully acknowledge Doug Boucher of Union of Concerned Scientists; Matthew Hansen of the University of Maryland; Ronald McRoberts of the U.S. Department of Agriculture Forest Service; Sassan Saatchi of the National Aeronatics and Space Administration Jet Propulsion Laboratory and University of California, Los Angeles Institute for Environment; Andreas Tveteraas of Norway’s International Climate and Forest Initiative; and Daniel Zarin of the Climate and Land Use Alliance for their useful inputs and suggestions for this policy brief.

We also thank the additional authors of the two seminal papers that provided the impetus for this collaboration:

Silvia Petrova, Winrock International Ecosystem Services Unit

William Salas, Applied Geosolutions, LLC

Peter V. Potapov, University of Maryland, Department of Geographical Sciences

Alexander Lotsch, The World Bank Group

Scott J. Goetz, Wayne S. Walker, Nadine T. Laporte, Mindy Sun, Joseph L. Hackler, Pieter S.A. Beck, and Sudeep Samanta, Woods Hole Research Center

Damien Sulla-Menashe and Mark A. Friedl, Boston University, Department of Geography and Environment

Ralph Dubayah, University of Maryland, Department of Geographical Sciences

The authors thank Mary Paden for her assistance with editing, Wenceslao Almazan for his assistance with graphic design, and Gaëlle Callnin and CMO & Client Strategy for their assistance with translation. The authors also thank Michael Lesnick, Sarah Walen, Mallorie Bruns, Liz Duxbury and Selena Elmer of Meridian Institute for organizing and facilitating the process that produced this policy brief.

This policy brief was made possible with financial support from the Government of Norway’s International Climate and Forest Initiative and the Climate and Land Use Alliance.


ACRONYMS CO2 Carbon dioxide

COP Conference of Parties

FAO Food and Agriculture Organization of the United Nations

Gt CO2 yr-1 Gigatons of carbon dioxide per year

GOFC-GOLD Global Observation of Forest and Land Cover Dynamics

IPCC Intergovernmental Panel on Climate Change

LiDAR Light Detecting and Ranging

Pg C yr-1 Petagrams of carbon per year

UNFCCC United Nations Framework Convention on Climate Change

REDD+ Reducing Emissions from Deforestation and Forest Degradation, and the Role of Conservation of Forest Carbon Stocks, Sustainable Management of Forests and Enhancement of Carbon Stocks

WHRC Woods Hole Research Center

4

1. INTRODUCTIONOur recently published estimates of carbon emissions from tropical deforestation appear to differ by a factor of three. A study by Baccini et al., published by a team at Woods Hole Research Center (WHRC), estimated gross emissions of carbon from forest land use and land-use change as 2.2 Pg C yr-1 (8.1 Gt CO2) over the period 2000 to 2010. Another study by Harris et al., published by a team from Winrock International, reported much lower gross carbon emissions from tropical deforestation between 2000 and 2005 of 0.81 Pg C yr-1 (3.0 Gt CO2 yr-1). The apparent disparity between the two estimates, 0.81 vs. 2.2 Pg C yr-1, was both surprising and troublesome for our understanding of the global carbon balance and for REDD+ policymakers.3

Here, we identify the major reasons for the differences in our estimates. To make a more precise comparison, we then align the scope of our analyses for the two methodologies employed. Doing so shows that estimated emissions of carbon from deforestation at the pantropical scale are remarkably similar, despite the use of different underlying data sets and models. However, this convergence does not extend to our estimates of where these emissions have occurred.

2. MAIN DIFFERENCESEmissions from Deforestation vs. from Land-Use ChangeThe goal of the Harris et al.4 paper was to provide spatially explicit estimates of committed emissions from gross loss of forest cover (i.e., deforestation) across the tropics with statistically derived estimates of uncertainty. The authors’ intent was to provide an unbiased historic benchmark of carbon emissions from gross tropical deforestation, derived from remote sensing data, against which future emissions could be measured. Harris et al. used remote sensing observations of gross forest cover loss to below 25% tree cover and assumed that these observed changes constituted deforestation events. No attempts were made to attribute these changes in land cover to specific land uses. The advantages of this approach were the consistency, transparency, and relatively fine spatial resolution of the estimates.

Because land-cover changes were sampled in individual pixels down to 30 m resolution across the tropics, the approach of Harris et al. allowed improved co-location of estimated carbon stocks with the forest areas actually lost. Emission estimates, as

3 Zarin (2012). 4 Data available at: http://appliedgeosolutions.com/science-paper.html.

well as related uncertainties, were published by Harris et al. at a spatial resolution of 18.5 km. It is likely that the Harris et al. analysis captured most forest land-use changes that result in a large change in canopy cover, such as when forest is cleared for cropland or pasture. Their analysis also likely captured some large logging operations (for industrial wood harvest), some shifting cultivation, and some natural disturbances, such as fire. However, the proportion of total emissions among the land-use changes captured in the estimates cannot be known with certainty.

The goal of Baccini et al.5 was to update the record of net emissions from tropical forest land use and land-use change. To do this, the authors used satellite-derived products on forest carbon stocks and data reported to the Food and Agriculture Organization of the United Nations (FAO) through 2010. Baccini et al. used a bookkeeping model6 that provided annual estimates of carbon emissions and removals from the terrestrial biota resulting from changes in land use. The bookkeeping model tracks annual per-hectare changes in carbon stocks when (1) forest area is cleared for cropland, pasture, or shifting cultivation; (2) forests are harvested; (3) plantations are established; and (4) agricultural lands are abandoned and returned to forest. The sum of all changes in carbon for all areas under management defines the annual net flux of carbon from all changes in forest land use, reflecting both carbon emissions to, and removals from, the atmosphere. This net flux takes into account the fact that the fallow phases of shifting cultivation and post-harvest regrowth of forests can sequester substantial amounts of carbon, partly or wholly offsetting the previous emissions (Figure 1).

Gross emissions from all forest land use and land-use change reported by Baccini et al., reflecting only emissions and disregarding removals of carbon, were estimated as the sum of emissions resulting from clearing for cropland, clearing for pasture, clearing as part of shifting cultivation cycles, and harvesting of fuelwood and industrial timber (Figure 1). Data on the extent of land use for recent decades are those reported by individual countries to FAO, including estimates of net changes in natural forest area and in planted forests (from the FAO Global Forest Resource Assessments, released every five years), and these data lack estimates of their uncertainty. In the bookkeeping model, the area-change data were distributed among cropland and grazing land using information reported in FAOStat. Data on the extent of shifting cultivation are not available across the pantropics, so instead Baccini et al. made the assumption that if FAO statistics reported larger losses of natural forests than the sum of increases in cropland and grazing land (as reported in the FAOStat database) in

5 Data available at Woods Hole Research Center: http://whrc.org/mapping/pantropical/carbondataset_form.htm.

6 Houghton et al. (1983).


Figure 1. Comparison of Gross Emissions and Gross Removals of Carbon from Land-Use Processes.

Note: Emissions and removals for Baccini et al. are reported in this figure by land-use process rather than by carbon pool, as reported in the original paper. Numeric values displayed in the bars are in units of Pg C yr-1 .

a given year, then these additional losses in forest area represented lands entering shifting cultivation cycles. Data on rates of fuelwood harvest and industrial wood harvest were also obtained from FAOStat. Baccini et al. did not define deforestation explicitly and reported emissions by carbon pool rather than by land-use process (Figure 1). This caused Harris et al. to conclude that the sum of the individual components of gross emissions (2.2 Pg C yr-1 or 8.1 Gt CO2e yr-1) represented the most appropriate value to compare against their estimate of gross emissions from deforestation (0.8 Pg C yr-1 or 3.0 Gt CO2 yr-1).

The advantage of the Baccini et al. approach is that all forest land use and land-use changes, including those considered as forest degradation and carbon stock enhancements in the REDD+ negotiations, are included and are broken down by driver of land-use change (e.g., clearing for cropland, timber harvesting). The disadvantages of this approach are that country estimates are often internally inconsistent

(e.g., different agencies may be responsible for reporting different statistics, thus values do not align); reported data are of variable quality and unknown accuracy; the estimation of land-use change is not a straightforward process due to the integration of many different datasets; and the area-change data cannot be co-located with forest carbon-stock data. Baccini et al. compensated for this last disadvantage by weighting the mean stock data for each region by those areas experiencing deforestation.

Different Spatial Scales In the Harris et al. analysis, estimates of forest area loss, carbon emissions, and associated uncertainties were calculated at the 18.5 km block scale, where each block represented approximately 350 km2 of land area. Block-scale estimates were then aggregated to the larger scales of interest (e.g., country, continental, pantropical). With respect to the spatial scale, Harris et al. took a “bottom up” approach to estimating emissions (and associated errors) by aggregating individual blocks. The Harris et al. group introduced a procedure for detailed accounting of uncertainty at the 18.5 km block scale and set forth a model of how this can be done in the future with higher resolution datasets.

In contrast, Baccini et al. took a “top down” approach with respect to spatial scale. Emission estimates were generated for eight large regions across the tropics (e.g., sub-Saharan Africa, South America) rather than for individual pixels or blocks. Although the bookkeeping model can be applied at any spatial resolution, Baccini et al. took a conservative approach and identified the regional and continental scales as minimum reporting units. The uncertainties associated with the carbon emissions reported by Baccini et al. were indicated by evaluating emission estimates based on model simulations of different scenarios using alternative data.

Different Carbon Stock MapsHarris et al. used a map of aboveground biomass (1-km resolution) derived by Saatchi et al. (2011)7 using methods based on satellite observations and calibrated with ground studies.8 For consistency with the forest-loss product used in their analysis,9 Harris et al. considered pixels in the biomass map as candidate pixels for potential deforestation in their model only if tree cover in the pixel was higher than 25% in the year 2000. Belowground biomass was estimated from aboveground biomass using an empirically derived allometric equation.10 Harris et al.’s emission estimates included losses of biomass carbon only; other carbon pools (including carbon 7 Saatchi et al. (2011). 8 Ibid. 9 Hansen et al. (2010).10 Mokany et al. (2006).

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6

land-use change occurs. Parameters for carbon response curves are defined individually for each ecosystem type within a given geographic region. The bookkeeping approach does not use the concept of committed emissions but instead includes model assumptions about the proportion of biomass and soil carbon that is converted to atmospheric carbon dioxide each year. Each year’s emission estimate therefore includes some emissions from past land use changes (i.e., “legacy” emissions) as well as a fraction of the emissions from the current year’s land-use change events. The remaining emissions are delayed to subsequent years to account for future decomposition processes.

3. TOWARD CONSENSUS For this paper, we aligned the scopes of the two analyses to facilitate a direct comparison between emissions estimates. We found that despite the use of different assumptions and independent data sets for estimating forest area change and forest biomass carbon stocks, estimates of gross carbon emissions from gross deforestation for the period 2000–2005 are identical at the pantropical scale: 0.8 Pg C yr-1 (2.9 Gt CO2 yr-1) for both studies, excluding emissions from mineral soils and peat.

We arrived at this conclusion using the following common framework:

1. The Harris et al. deforestation emissions estimate was assumed to be most comparable to the sum of the Baccini et al. clearing for cropland, clearing for grazing land, and (first-time) clearing for shifting cultivation emission estimates (i.e., these were considered to be “deforestation” whereas other land-uses of wood harvest and the rotational cycles of shifting cultivation were considered to be “forest degradation”);

2. Both groups tested the impacts of including and excluding soil carbon in the estimates;

3. Both groups estimated emissions for the same time period (2000–2005);11

4. Both groups estimated only gross (not net) emissions.

We recognize that forest degradation processes contribute a significant and highly uncertain quantity of carbon to the atmosphere on an annual basis in addition to the emissions from deforestation alone. We have not included these

11 Baccini et al. used a time period of 10 years, but for this paper, WHRC redid its analysis to include only the years 2000-2005 to match the period used by Harris et al. The Winrock group did not change anything in its analysis published originally as Harris et al.

in mineral soils, peat, and dead wood) were not included due to a lack of reliable information regarding their spatial extent, distribution, and magnitude.

Carbon stocks in aboveground biomass were estimated by Baccini et al. using a combination of field measurements co-located with Light Detecting and Ranging (LiDAR) observations and satellite imagery. The map, produced at 500-m spatial resolution (vs. 1-km as used by Harris et al.), estimates carbon stocks for the year 2007–2008 rather than the year 2000, and no forest cover mask was used to exclude areas that had been deforested prior to the year 2008 from the forest carbon stock estimates. Instead of using pixel-based biomass estimates directly (as Harris et al. did) to estimate emissions, Baccini et al. “weighted” their biomass values per region of the bookkeeping model to represent the average biomass of deforested lands using the same 2000–2005 forest-loss product used by Harris et al. to identify areas of deforestation.7 Belowground biomass was assumed to be 20% of aboveground biomass. Soil carbon emissions were also included in the Baccini et al. analysis, and like Harris et al., they did not include emissions from peat or from the dead-wood pool.

Different Carbon Emission ModelsHarris et al. multiplied the observed quantity of forest area lost between 2000 and 2005 in each 18.5 km block by the estimated forest carbon stock in aboveground and belowground biomass in the block in the year 2000. Because the exact locations of forest loss within a given block were unknown, Harris et al. repeated a randomization procedure in which forested 1-km pixels within a given 18.5 km block were selected randomly until the forest-loss quota for the block was met. The total carbon values of selected 1-km pixels were then summed across the block to derive an emissions estimate. This procedure was repeated 1,000 times per block to derive a “best estimate” as well as an uncertainty range for carbon emissions occurring within each block. Harris et al. estimated average committed emissions over a single five-year time interval (2000–2005) for which remote sensing observations on gross forest cover loss were available. Because Harris et al. focused on gross emissions, they also simplified the accounting by assuming that all biomass carbon in the deforested landscape was converted immediately to atmospheric carbon dioxide, that is, their estimates reflected “committed” emissions to the atmosphere that will occur regardless of future land-use or land-management decisions.

In contrast, Baccini et al. reported annual emissions averaged over a 10-year time interval (2000–2010). They used a carbon bookkeeping model based on a series of carbon response curves, one curve for each type of land-use change. Each curve, defined by up to 22 parameters, simulates the annual fluxes of carbon to and from the atmosphere after a given


processes in this comparison because the Winrock team12 did not address them in its original analysis due to lack of adequate quality data at the pantropical scale.

Our consensus on deforestation emissions was achieved by considering only forest biomass carbon stocks; emission estimates differ by ~10% when soil carbon emissions are incorporated into the analyses—0.87 vs. 0.96 Pg C yr-1 (3.2 vs. 3.5 Gt CO2 yr-1) for the Winrock group and the WHRC group, respectively. The Winrock team incorporated emissions from soil carbon using spatial estimates of soil carbon stocks to a depth of 30 cm from the World Harmonized Soil Database and Intergovernmental Panel on Climate Change (IPCC) factors related to the fraction of total soil carbon that is emitted upon clearing forest for a new land use under given management conditions (e.g., conversion to agriculture with full tillage and medium nutrient inputs).13 They also developed assumptions about the proportion of the total forest area lost per 18.5 km block where soil carbon was likely to be impacted. The WHRC team estimated ecosystem-level soil carbon stocks to a depth 12 Harris et al. and Baccini et al. refer only to information presented in the

original published papers. The research group names Winrock and WHRC are included in this section to distinguish the work done as part of this comparison effort from the original analyses.

13 Intergovernmental Panel on Climate Change (2006).

of one meter14 and modeled a loss of 25%15 when natural soils were converted to cropland. These methodological differences caused the Winrock team’s emission estimates to increase less than the WHRC’s team upon incorporation of soil.

Although we have reached consensus in our emissions estimates from gross deforestation at the pantropical scale (excluding soil carbon emissions), significant differences remain for sub-Saharan Africa and South and Southeast Asia (Table 1). Estimates are similar between the two analyses for Latin America, likely due to the large proportion of South American emissions originating in Brazil and to the fact that Brazil reports up-to-date forest area loss data to FAO derived from remote sensing imagery similar to that used in the Winrock analysis. In other words, about half of our consensus is likely due to the use of similar data sets on forest area change in Brazil (Table 1). The other half of our consensus is more coincidental, as it results from differences in sub-Saharan Africa and South and Southeast Asia between the two analyses that cancel each other out. The Winrock team estimates higher carbon stocks and rates of forest loss, and consequently higher emissions from South and Southeast 14 Post et al. (1982). 15 Schumacher and Freibauer (2011).

Table 1. WHRC and Winrock Teams’ Estimates of Forest Carbon Stocks in Areas of Deforestation, Rates of Forest Loss, and Carbon Emissions, by Region.

WHRC team Winrock team

Mean forest carbon stocks in deforested blocks (Mg C ha-1)

Sub-Saharan Africa 40 61

Latin America 88 90

South and Southeast Asia 56 144

Pantropics 69 95

Rates of gross forest loss (103 ha yr-1)

Sub-Saharan Africa 3,610 1,889

Latin America 4,882 4,873

South and Southeast Asia 1,230 1,785

Pantropics 9,722 8,547

Gross carbon emissions (Pg C yr-1) *

Sub-Saharan Africa 0.23 0.11

Latin America 0.47 0.44

South and Southeast Asia 0.11 0.26

Pantropics 0.81 0.81 Note: Estimates were made by the WHRC and Winrock groups using the common framework referenced at the beginning of Section 3. * Carbon emission estimates reflect emissions from biomass only.

8

Asia, whereas the WHRC team estimates emissions from sub-Saharan Africa that are more than twice as high as the estimates of the Winrock team, due largely to higher estimates of the rate of forest loss in this region, even though their estimate of regional carbon stocks is lower.

4. CONCLUSIONS AND IMPLICATIONS FOR REDD+ MONITORINGA systematic approach of spatially matching carbon stocks with the forest areas that are cleared and reporting resulting emissions and uncertainty at high spatial resolution, as done by the Winrock team, is best suited for estimating carbon emissions from gross deforestation. However, the WHRC approach of including additional carbon fluxes associated with land use and land-use change in tropical forests is critical for developing a full accounting framework.

Harris et al. highlight the need for reporting emissions estimates at the subnational scale and demonstrates a valuable approach to rigorously account for uncertainty in all components of an emissions analysis. Remote sensing technology and methods are more than adequate to monitor gross deforestation,16 which, for many developing countries, is the most significant component of carbon emissions from forest land use. Many governments are advancing in their access to data and in their technical capabilities to improve emissions estimates at national and subnational levels. With new deforestation estimates based on higher resolution remote-sensing data, estimates of carbon emissions from deforestation will improve enabling countries to meet more rigorous reporting requirements with reasonable certainty. Further improvements will occur as countries develop their own forest carbon stock estimates to use with the improved maps of deforestation.

The WHRC group highlights shifting cultivation and fuelwood extraction as causes of forest degradation. Given the significance of these net emissions (difference between the losses and gains by regrowth), more attention to monitoring these activities is needed. Although newer methods have emerged for monitoring forest degradation from timber-extraction operations using both LiDAR and medium- resolution imagery,17,18,19 these methods, and the

16 Global Observation of Forest and Land Cover Dynamics (2011). 17 Asner (2009). 18 Asner et al. (2005). 19 Souza and Roberts (2005).

corresponding capacity to implement them, need to be made more available to countries. Robust and cost-effective methods also need to be developed for monitoring shifting cultivation – not only its extent but also the changes in practice and the associated impacts on carbon dynamics (e.g., lengthening or shortening rotation of the forest-fallow cycle).

The focus of comparing the Winrock and WHRC studies has been on emissions from deforestation, but, as mentioned above, other emission sources are also important for REDD+. Emissions due to peat drainage and peat fire for an overlapping time period have been estimated as 1.0 Gt CO2 yr-1 (0.27 Pg C yr-1).20,21 We have shown in section 3 that emissions from mineral soils after deforestation account for approximately 0.3 Gt CO2 yr-1 (0.10 Pg C yr-1). The IPCC advises that emissions from forests remaining as forests (i.e., forest degradation) can be estimated as the difference between the losses of carbon due to degrading activities and the gains of carbon from forest regrowth. As reported in Baccini et al., the net change in carbon stocks due to shifting cultivation and fuelwood harvest is about 1.0 Gt CO2 yr-1 (0.27 Pg C yr-1). Thus the total of the other emission sources important to REDD+ is approximately 2.3 Gt CO2 yr-1 (0.64 Pg C yr-1), though this estimate is highly uncertain due to the lack of reliable data.

5. AGREEMENT ON A PANTROPICAL BENCHMARKREDD+ negotiators within the United Nations Framework Convention on Climate Change (UNFCCC) have previously discussed, but thus far avoided, setting an explicit target for emission reductions. Other groups, including the European Commission, the Informal Working Group on Interim Finance for REDD+, and the UK government’s Eliasch Review, have converged around a target of cutting tropical deforestation by 50% by 2020, but the benchmark against which this target should be evaluated has been unclear. By achieving consensus within the scientific community that emissions from tropical deforestation between 2000 and 2005 were 3.0 ± 1.1 Gt CO2 yr-1 (0.8 ± 0.3 Pg C yr-1), researchers have given policymakers an unbiased, historic benchmark to use in discussing agreement on a target of reducing emissions from gross tropical deforestation to below 1.5 Gt CO2 yr-1 (0.4 Pg C yr-1) by 2020. If policymakers prefer to use a more inclusive target by incorporating other emission sources important to REDD+ in addition to deforestation, then the 50% target increases to about 2.6 Gt CO2 yr-1 (0.7 Pg C yr-1) by 2020.

20 Van der Werf et al. (2008). 21 Hooijer et al. (2010).


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Meridian Institute is a not-for-profit organization whose mission is to help people solve problems, make informed decisions, and find solutions to some of society’s most complex and controversial issues. Meridian’s mission is accomplished through applying collaborative problem-solving approaches including facilitation, mediation, and other strategic consultation services. Meridian works at the local, national, and international levels and focuses on a wide range of issues related to natural resources and environment, science and technology, agriculture and food security, sustainability, global stability, and health. For more information, please visit www.merid.org.

Meridian Institute1920 L Street NW, Suite 500Washington, DC 20036 USAPhone: +1 202-354-6440Fax: +1 202-354-6441www.merid.org

ABOUT MERIDIAN INSTITUTE

progress toward a consensus on carbon emissions...

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