Estimating the impacts of conservation on ecosystemservices and poverty by integrating modelingand evaluationPaul J. Ferraroa,1, Merlin M. Hanauerb,1, Daniela A. Mitevac,d, Joanna L. Nelsone,f, Subhrendu K. Pattanayakg,Christoph Nolteh, and Katharine R. E. Simsi,j

aDepartment of Economics, Andrew Young School of Policy Studies, Georgia State University, Atlanta, GA 30319; bDepartment of Economics, Sonoma StateUniversity, Rohnert Park, CA 94928; cThe Nature Conservancy, Fort Collins, CO 80524; dInstitute on the Environment, University of Minnesota, St. Paul,MN 55108; eThe Nature Conservancy, Arlington, VA 22203; fStanford Woods Institute for the Environment, Stanford, CA 94305; gSanford School ofPublic Policy and Nicholas School of the Environment, Duke University, Durham, NC 27708; hSchool of Natural Resources and Environment, Universityof Michigan, Ann Arbor, MI 48109; and Departments of iEconomics and jEnvironmental Studies, Amherst College, Amherst, MA 01002

Edited by Stephen Polasky, University of Minnesota, St. Paul, MN, and approved February 4, 2015 (received for review May 5, 2014)

Scholars have made great advances in modeling and mappingecosystem services, and in assigning economic values to theseservices. This modeling and valuation scholarship is often discon-nected from evidence about how actual conservation programshave affected ecosystem services, however. Without a strongerevidence base, decision makers find it difficult to use the insightsfrom modeling and valuation to design effective policies andprograms. To strengthen the evidence base, scholars have advancedour understanding of the causal pathways between conservationactions and environmental outcomes, but their studies measureimpacts on imperfect proxies for ecosystem services (e.g., avoid-ance of deforestation). To be useful to decision makers, theseimpacts must be translated into changes in ecosystem servicesand values. To illustrate how this translation can be done, we esti-mated the impacts of protected areas in Brazil, Costa Rica, Indonesia,and Thailand on carbon storage in forests. We found that protectedareas in these conservation hotspots have stored at least an ad-ditional 1,000 Mt of CO2 in forests and have delivered ecosystemservices worth at least $5 billion. This aggregate impact masksimportant spatial heterogeneity, however. Moreover, the spatialvariability of impacts on carbon storage is the not the same asthe spatial variability of impacts on avoided deforestation. Thesefindings lead us to describe a research program that extends ourframework to study other ecosystem services, to uncover the mech-anisms by which ecosystem protection benefits humans, and totie cost-benefit analyses to conservation planning so that we canobtain the greatest return on scarce conservation funds.

parks | avoided emissions | tropical forest | sequestration |quasi-experiment

To inform decision makers about how they can best allocateresources to maintain and enhance ecosystem services, scholars

need to develop a better understanding of how policy interven-tions actually affect the supply of ecosystem services. In the lasttwo decades, scholars have made important advances in defining,measuring, and valuing ecosystem services across time and space(1–7). These measures and values have in turn been used asconservation planning and resource management decision tools(8–11). The degree to which policies and programs have affectedthese services and values in the past remains poorly understood,however (12, 13). Without an evidence base for the impacts ofreal policies and programs on ecosystem services, the insights frommodeling and valuation are not as informative to decision makersas they could be.A separate and more recent literature focuses on estimating

the impacts of conservation policies and programs on environ-mental and social outcomes (reviewed in refs. 14 and 15). Scholarsworking in this impact evaluation literature strive to eliminaterival explanations for the empirical patterns observed, so that any

correlations between programs and outcomes can be confidentlyattributed to causal relationships; however, these studies do notestimate impacts on ecosystem services, but rather estimateimpacts on human behaviors (e.g., deforestation, fire, resourceextraction) assumed to be correlated with ecosystem service stocksand flows. In other words, they focus on intermediate variables onthe assumed causal chain between the policy interventions andsubsequent changes in ecosystem services. Given the substantialevidence that ecosystem service stocks and flows are highly spa-tially variable and nonlinear, a close correlation between behav-ioral impacts and ecosystem service impacts cannot be assumed.For example, interventions that generate the most avoided de-forestation or avoided fires might not be the same ones that gen-erate the most additional carbon storage or hydrological services,or generate the highest economic return on investment. We needdirect empirical estimates of program impacts on ecosystemservices and their economic values.Scholars in these two literatures—impact evaluation and

ecosystem service modeling and monetization—need to collab-orate (12). This paper illustrates how impact evaluators havean intellectual architecture for estimating policy and programimpacts on human behaviors like land conversion, and also forexplaining how these effects are moderated by time-varyingand time-invariant (i.e., fixed) site characteristics. Ecosystem

Significance

Research shows how the potential services from ecosystemconservation can be modeled, mapped, and valued; however,this integrative research has not been systematically applied toestimate the actual impacts of programs on the delivery ofecosystem services. We bridge this divide by showing howprotected areas in Brazil, Costa Rica, Indonesia, and Thailandstore carbon and deliver ecosystem services worth at least$5 billion. Impacts on carbon are associated with poverty ex-acerbation in some settings and with poverty reduction in others.We describe an agenda to improve conservation planning by(i) studying impacts on other ecosystem services, (ii) uncoveringthe mechanisms through which conservation programs affecthuman welfare, and (iii) more comprehensively comparingcosts and benefits of conservation impacts.

Author contributions: P.J.F., M.M.H., and J.L.N. designed research; P.J.F., M.M.H., D.A.M., J.L.N.,S.K.P., C.N., and K.R.E.S. performed research; P.J.F., M.M.H., and D.A.M. analyzed data; and P.J.F., M.M.H., and S.K.P. wrote the paper.

The authors declare no conflict of interest.

This article is a PNAS Direct Submission.1To whom correspondence may be addressed. E-mail: hanauer@sonoma.edu or pferraro@gsu.edu.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1406487112/-/DCSupplemental.

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service modelers have another intellectual architecture formodeling and mapping the relationships between ecosystemservice flows and time-varying attributes, such as land usechange. Economists doing monetization and valuation havea third architecture for estimating the value of changes inecosystem services. An integrated architecture would tracecausal relationships between policies and ecosystem services,and include the option to conduct an economic valuation ofthe impacts as an estimate of the societal benefits from thepolicies. By integrating these architectures in single studies ofreal policies, scholars would be able to produce far morepolicy-relevant scientific evidence than that currently pro-duced in their separate scientific spheres.Here we provide an illustration of such an integration, and the

insights that it can provide, by examining the impacts of pro-tected area networks on carbon storage in forested ecosystems ofthe Brazilian Amazon, Costa Rica, Indonesia, and northern andnortheastern Thailand. We focus on forests because the pro-portion of carbon stored in forests composes 70–80% of totalterrestrial carbon (16). More critically, great strides have beenmade in measuring and monitoring carbon stocks and carbonemissions from forests (17, 18), but the degree to which thesestocks and emissions have been affected by environmental poli-cies and programs is poorly understood (19).Governments often use protected areas to protect ecosystems

and their services (20). Protected areas are also integral parts ofstrategies to reduce carbon emissions from deforestation anddegradation, such as the REDD+* program (21, 22). Recentstudies have estimated the effects of terrestrial protected areanetworks on deforestation (23–27), fire (28), regrowth (29), andpoverty (30–32), but not their effects on ecosystem services.Explicitly estimating effects on ecosystem services is important,because there is no reason to assume that the spatial variabilityof effects on ecosystem services matches the spatial variability ofeffects on human behaviors; for example, the spatial variability ofcarbon is highly heterogeneous (18, 33), and the locations wherecarbon density is highest might not be the same locations whereprotected areas have the greatest effects on land use.

Study DesignTo measure carbon consistently across all four countries, we usednew data on the global distribution of aboveground woody bio-mass in the tropics in 2008 (18) and published formulas for con-verting biomass into carbon stocks (34). To estimate the impact ofprotection on carbon storage in the protected units, known as the“average treatment effect on the treated” (ATT), one must es-tablish what carbon storage would have been in the absence ofprotection. To estimate this counterfactual outcome, we usedmatching to select unprotected control units that are similarat baseline to protected units in terms of distributions of keycovariates believed to affect both carbon density and selectioninto protection (Materials and Methods and SI Appendix, TablesS1–S10 and Figs. S1–S6). For example, protected areas are oftenplaced on land less suited for agriculture (35), which also mighthave lower carbon density (18).Protected areas can affect carbon storage in two ways: by

preventing the loss of biomass (avoided emissions) and by per-mitting the growth of biomass (additional storage, or seques-tration). We estimated the effect of both pathways (describedformally in SI Appendix). In developing countries, efforts to useprotected areas to reduce carbon emissions from forests are

controversial because of concerns about negative impacts on therural poor (31); thus, we also contrasted the estimated effects ofprotected areas on carbon with their estimated effects on povertyin the same countries.We estimated the impacts for the protected area networks

established before 1997 in Costa Rica, before 1985 in Thailand,between 1988 and 2008 in Indonesia, and between 2000 and 2008in the Brazilian Amazon (Materials and Methods and SI Appendix).We selected these four countries for two reasons. First, Braziland Indonesia contain 35% of the total carbon stored in tropicalforests and produce the largest emissions from forest loss (18, 36).Second, our interdisciplinary team had the requisite data tocredibly estimate the effects of protected areas on carbon in allfour countries, and to contrast those effects with the effects onpoverty in neighboring communities in three of the four countries.All four countries have been the subjects of previous studies onthe impact of protected areas on deforestation and regrowth(23, 25, 29, 32). Two of them have been the subjects of publishedstudies on the impact of protected areas on poverty in neigh-boring communities (31, 32), and on the moderators of the forestand poverty impacts (26, 37).The greatest challenge in retrospective studies of program

impacts on ecosystem services is the lack of preprotectionmeasures of ecosystem services. In our context, we cannotmeasure preprotection carbon densities. As is the case for mostecosystem services, widespread and accurate mapping of carbondensities has become available only recently. To address thechallenge of missing carbon baselines, we have developed anapproach that permits temporal variation in carbon density,which has been reported elsewhere (33). We assumed that aftermatching on observable characteristics that affect both landuse and where protected areas are assigned, the unobservable,baseline carbon values of matched protected and unprotectedforest parcels were the same, on average. Under this assump-tion, the current average carbon density of the matched un-protected parcels provides a good estimate of the counter-factual average carbon density of the protected parcels had theynot been protected.In contrast to approaches that assume no temporal variation in

carbon, or require imputation of missing baseline carbon values,our approach provides an indirect test (i.e., a “placebo test”) toreject the key untestable assumption on which the approachrelies. If matching were effective in making baseline carbon den-sities similar among protected and unprotected parcels, then italso should be effective in making current carbon values similaramong unprotected parcels. In other words, we took the matchedunprotected parcels (i.e., the control parcels matched to theprotected parcels) and match them to other unprotected parcels.We then tested the null hypothesis that the mean carbon valuesare equal in these two groups. Large and statistically significantdifferences would cast doubt on our assumption that the distri-butions of baseline carbon densities of protected and unprotectedparcels are equivalent after matching (SI Appendix, Table S11).Carbon storage is a global service (i.e., contribution to climate

change mitigation), and thus offers a relatively straightforwardcase for calculating its value, or shadow price. The “social cost ofcarbon” (SCC) provides an estimate of the monetized damagesfrom an incremental increase in carbon emissions in a given year.SCC estimates vary widely, depending on the discount rate ap-plied and the particular integrated assessment model and pa-rameterization used (38, 39). Because we do not have repeatedobservations of carbon density over time, we cannot know theexact year when a particular avoided ton of CO2 emissions fromprotection would have been emitted, or when a particular ad-ditional ton of CO2 would have been sequestered. Impactsachieved earlier in the study periods would be worth less thanimpacts achieved later. Moreover, we cannot predict whetherthese impacts are permanent (i.e., the stored carbon will not be

*The most recent definition of REDD+ refers to “reducing emissions from deforestationand forest degradation in developing countries, and the role of conservation, sustain-able management of forests, and enhancement of forest carbon stocks in developingcountries” (16th edition of Conference of the Parties of the United Nations FrameworkConvention on Climate Change).

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emitted back into the atmosphere). Thus, to be conservative, weused a value of $5/ton of CO2, similar to payments in currentREDD+ arrangements (40). For readers who believe that the SCCshould be much higher, we also present results using $100/ton.

ResultsEstimated Impacts. In Fig. 1, the estimated ATTs are measures ofadditional storage per hectare inside protected areas. Based ont tests of the null hypothesis of zero impact, the estimates aresignificantly different from zero in all four countries (SI Appendix,Table S12). The bars in Fig. 1 represent the total estimated addi-tional amount of CO2 storage induced by the protected areas. Forcomparison, we used a recent study (36) to estimate the medianannual gross CO2 emissions from deforestation in these fourcountries for the period 2000–2005 (includes losses from below-ground biomass): 1,247.8 Mt CO2 y

−1 for Brazil, 3.67Mt CO2 y−1 for

Costa Rica, 385.4 Mt CO2 y−1 for Indonesia, and 58.7 Mt CO2 y

−1

for Thailand. Thus, for example, Brazil’s post-2000 protected areasinduced additional CO2 storage by 2008 that was equivalent to ap-proximately two-thirds of 1 year’s emissions from deforestation inthe early 2000s. Fig. 1 also presents monetary values of the totaladditional amount of CO2 stored. The aggregate amounts on the leftaxis could instead be multiplied by a different estimate of the SCC, ifso desired.In all four countries, the placebo test results are consistent

with our implicit assumption that the distributions of baselinecarbon densities of protected and unprotected parcels are equiv-alent after matching (SI Appendix, Table S11). We tested for localdisplacement (i.e., local leakage to nearby unprotected areas)and were unable to detect any (SI Appendix, Table S13). The mostlikely sources of measurement error would hinder detection ofany treatment effects, and thus measurement error is unlikely tobe a rival explanation for our results (SI Appendix). Finally, weconducted tests to assess the sensitivity of our results to poten-tial hidden bias from a potential unobserved confounder (SIAppendix, Table S12).

Moderators of Impacts. To illustrate the heterogeneity of theimpacts of protected areas on CO2 storage, and how this het-erogeneity may differ from the heterogeneity of impacts onavoided deforestation, Fig. 2 shows how impacts vary conditionalon slope and distance to major cities, holding other factors con-stant. These two covariates have been used in a previous anal-ysis of moderators of protected areas’ impacts on avoided de-forestation (26) because they are globally available and capturelandscape features that affect the returns to agriculture and log-ging, the primary drivers of land use change in our four countries.A gentle slope facilitates land use, as does proximity to urbanmarkets. Slope also has been identified as an important pre-dictor of carbon density (41).As noted above, we also sought to contrast the effects of

protection on carbon and on poverty. Because we did not havepoverty data for Brazil, we do not include its analysis of het-erogeneity in Fig. 2; the shapes of its carbon storage graphs aresimilar to those of Costa Rica and Thailand (SI Appendix, Figs.S7 and S8).In Fig. 2, the ways in which slope and distance to cities mod-

erate carbon and poverty impacts are not the same across thecountries. These results highlight how global-level analyses maymiss important heterogeneity of program impacts on ecosystemservices. Moreover, the slopes and distances associated with thegreatest poverty alleviation are not necessarily those associatedwith the most additional carbon storage.Comparisons with similar figures for avoided deforestation in

Costa Rica, Thailand (fig.1 in ref. 26), and Indonesia (SI Ap-pendix, Fig. S9) demonstrate that how impacts vary across spacedepends on whether the outcome is measured in terms of avoideddeforestation or additional carbon storage. For example, inThailand, protected areas’ impacts on avoided deforestation arelowest at approximately 100 km from cities, but their impacts onCO2 storage are greatest at that distance. Thus, impacts estimatedin terms of avoided deforestation are imperfect proxies for CO2storage impacts. In each country, the locations where protectedareas generate the most avoided deforestation are not necessarilythe areas with the highest carbon densities.

DiscussionResearch on forest carbon, like research on ecosystem services ingeneral, emphasizes measurement and monitoring (e.g., to facili-tate emissions reporting requirements); however, understandingwhich policies and programs work best to reduce carbon emissionsfrom land use change, and under what conditions they work best,is important as well. Much of the policy-related science on eco-system services evaluates how modeled (and usually hypothetical)changes in a generic conservation action would affect the quantityof one or more services, or people’s willingness to pay for changesin these services (42, 43). In these hypothetical scenarios, decisionmakers often are assumed to know where ecosystem conversion willoccur and then can assign “protection” that stops this conversion.Real conservation programs are quite different, however. For

example, there is ample evidence that protected areas and pay-ments for environment services are assigned to areas with below-average probability of conversion (35, 44). In addition, compli-ance with these programs might not be perfect. Moreover, themechanisms through which a conservation program achievesimpacts, the moderators of those impacts, and the costs ofachieving those impacts likely differ depending on the kind ofconservation program and its location. Thus, assuming a ho-mogenous intervention labeled “protection” may mislead us re-garding how to allocate scarce conservation resources. Scientistsneed to move beyond hypothetical scenarios and undertake im-pact evaluations of real-world policies aimed at delivering ecosys-tem services. Even the most sophisticated scenario modeling thatincorporates behavioral models of land user responses to policies

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(11) could be improved by incorporating insights from estimates ofactual behavioral responses to real conservation programs.Although the protected areas in our study were not established

with the intent of protecting carbon, they contributed to at leastan additional 1,000 Mt of CO2 stored. (The estimates ignorebelow-ground carbon and do not cover all protected areas;Materials and Methods.) The locations where protected areasgenerated the most additional carbon storage were not necessarilythe areas where they generated the most avoided deforestation.Moreover, although protected areas can reduce poverty in neigh-boring communities, our analysis highlights trade-offs if decisionmakers wish to use protected areas to attain more carbon addi-tionality or poverty reduction. To visualize these trade-offs, plannerscan use suitability maps (26) like the one for Costa Rica shown inFig. 3. Although there are locations where, based on past patternsof impacts, one would expect protected areas to contribute torelatively high amounts of additional carbon storage and povertyreduction (yellow in Fig. 3), these locations are not abundant;however, there are many areas where carbon storage goals may beachieved without exacerbating poverty (blue). These areas withthe greatest expected additionality from protection are not nec-essarily the areas with the greatest carbon density, however (SI

Appendix, Fig. S10). Suitability maps like these could be used inthe design of REDD+ programs aimed at reducing emissions fromforest degradation and deforestation while maintaining livelihoodsand reducing poverty. (More details on creating suitability mapsare provided in SI Appendix.)Estimates like those in Fig. 1 also can contribute to an im-

portant debate in negotiations over the design of REDD+ pro-grams: how, if at all, to acknowledge past investments to reducedeforestation and degradation. Most proposed REDD+ designsreward a country if it reduces its deforestation rate below a ref-erence level. The most commonly suggested reference level isa country’s recent national deforestation rate (45). Some haveargued that this design rewards historically “prolific deforesters”and punishes good stewards (46). Countries like Costa Rica haveargued that they should receive credit for protecting their forestsover the years (47). To adjust reference levels to take into accountpast conservation investments, decision makers must be able todistinguish countries that have low historical deforestation rates foreconomic reasons from countries with low historical deforestationrates because of conservation actions. Impact estimates like thoseshown in Fig. 1 can assist efforts to adjust REDD+ reference levelsbased on pre-REDD+ conservation actions.

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Fig. 2. Heterogeneous responses to protection. The solid and dashed lines represent the conditional ATT for additional CO2 storage and poverty reduction,respectively. The blue or red shaded areas around the lines represent the 95% pointwise confidence bands for the estimated conditional ATT. The solidhorizontal lines represent the zero impact line for CO2 storage (red) and poverty (blue). Poverty is measured by a census-based poverty index in Costa Rica (Aand B), a poverty headcount ratio in Thailand (C and D), and the percentage of households below the poverty line in Indonesia (E and F).

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Our study is a first step in a much broader research programfor integrating the growing literatures on impact evaluation andon ecosystem service modeling, mapping, and monetization.Ultimately, a focus on an array of services, contexts, and valueswill permit better integration of two key decision making tools:conservation planning (48, 49) and cost-benefit analyses. Belowwe highlight several extensions:

i) More services. Carbon storage in forests is arguably the sim-plest case for integration, because storage is immobile anda global public good. For such services as hydrological orpollination services, spatial interactions are much more im-portant, and the demands on the impact evaluation and mod-eling team will be more substantial. For similar reasons, thevaluation of regional services, such as water quality and quan-tity, or local services, such as nontimber forest products andecotourism, will require the use of nonmarket valuation de-signs that incorporate the characteristics of the beneficiarypopulations (2, 50). Extending this research to examining howprograms affect biodiversity, which may then affect ecosystemservices, also would be fruitful, and informative for the con-tentious debate about the appropriate focus of conservationinvestments: biodiversity versus ecosystem services (51–53).

ii) More places. More evidence from more countries is needed.Although carbon data exist at a global level, we believe thata single global-level study is ill-advised. Controlling for thecountry-specific selection processes through which someareas come to be protected cannot be accomplished withjust a few covariates available at a global level. Moreover,as observed in our study, analyses at smaller scales revealimportant policy-relevant patterns of heterogeneity.

iii) More policies. Protected areas are not the sole availabletools for ecosystem conservation. Similar studies should bedone on other types of conservation interventions, particu-larly payments for ecosystem services, which are explicitlyaimed at supplying ecosystem services.

iv) Linking conservation actions and human well being. Conser-vation actions can improve human welfare through variousmechanisms, including the maintenance and enhancementof ecosystem services. A recent study of these mechanismscould only estimate how protected areas affected povertythrough changes in land cover, rather than through changesin specific services that arose from the changes in land cover(54). Future attempts to quantify these mechanisms could

benefit from more careful and rigorous connections to themodeling and monetization literature. Clarifying the mech-anisms through which protected areas operate in each coun-try also could shed light on why we observe trade-offs insome locations between poverty reduction and carbon stor-age, and how we may reduce the magnitude of these trade-offs in the future through better program design.

Both conservation planning and cost-benefit analyses can usebetter information on the extent, type, and value of services frompast policies to forecast expected impacts in the future. For exam-ple, carbon values from Fig. 1 could be added to values from otherecosystem services and then compared with the opportunity costsof foregone land uses (including effects on livelihoods and localcultures) and protected area management costs (55, 56). Simi-larly, more elaborate versions of our suitability maps can be usedto improve conservation planning efforts to maximize expectedimpacts of conservation programs, subject to budget constraints.Ultimately, decision makers and ecosystem service scientists

want a strong evidence base to guide conservation actions. Thisevidence base can be built by combining the strengths of existingresearch areas in impact evaluation and in the modeling, mapping,and monetization of ecosystem services. With stronger evidenceand theories about how conservation programs affect couplednatural–human systems, scientists, policymakers, and practitionerscan determine how to best design policies for enhancing humanwelfare, while conserving species and habitats.

Materials and MethodsCarbon and CO2. Our data on carbon stocks came from the Pantropical Na-tional Level Carbon Stock Dataset of Woods Hole Research Center (18), whichwas created using a fusion of MODIS and LiDAR data calibrated with fieldmeasurements. Each 500-m2 pixel contains a measure of aboveground livewoody biomass in milligrams per hectare. We converted measurements ofaboveground live woody biomass (biomass) per hectare to CO2 storage perhectare by (i) converting biomass per hectare to tons of carbon per hectare(mg C ha−1 = mg biomass ha−1 · 0.5) (34) and (ii) converting tons of carbonper hectare to tons of CO2 per hectare (mg CO2 ha

−1 = mg C ha−1 · 3.67). Thefinal conversion is based on the atomic mass of carbon as a fraction of CO2:44/12 = 3.67. We assumed that all carbon is released into the atmosphere onbiomass removal, so that differences in observed and counterfactual levelsof CO2 are interpreted as the additional CO2 storage due to protection;a similar assumption has been used by other investigators (36).

Our study does not include protected area impacts on below-groundbiomass, on soil carbon, or on peat drainage and burning. Below-groundbiomass in tropical forests is much less than the aboveground biomass(6–33%) (57), but peat drainage and burning is an important source ofcarbon emissions in Indonesia (58). More details are provided in SI Appendix.

Units of Analysis. Brazil, carbon: 1-km2 parcels selected at random from landthat was forested in 2000.

Costa Rica, carbon: 3-ha parcels selected at random from either forested orunforested land at the relevant baseline year (1960 or 1986).

Costa Rica, poverty: 1973 census tracts (average area of 9 km2); povertydata from the 1973 and 2000 censuses.

Indonesia, carbon: 463-m forest or peatland parcels selected at randomfrom a 1988 land cover map.

Indonesia, poverty: Villages (average area of 22 km2); poverty data fromthe 2006 village census (PODES).

Thailand, carbon: 3-ha parcels selected at random from land that wasforested in 1973 in northern and northeastern Thailand, where the majorityof the protected areas are located.

Thailand, poverty: subdistricts (tambons; average area, 74 km2).More details are provided in SI Appendix.

Protection. Carbon analyses: A parcel is defined as protected if the parcel’scentroid lies within a national protected area.

Poverty analyses: In Costa Rica and Thailand, a unit (census tract, sub-district) is protected if at least 10% of the unit is covered by a protected area.(Units with coverage between 1% and 10% are excluded from the sample.) InIndonesia, a village is protected if it intersects with a protected area.

More details are provided in SI Appendix.

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Fig. 3. Carbon-poverty suitability map. The estimated conditional ATTs onCO2 storage additionality and poverty reduction in Fig. 2 are rescaled to fallwithin a range of 0 (low) to 10 (high). Locations where rescaled scores forboth CO2 and poverty additionality are above 6 are in yellow, and locationsin which protection is expected to exacerbate poverty are in black.

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Matching. We used one-to-one nearest-neighbor matching with replace-ment to select unprotected units that are similar to protected units on av-erage, based on observable covariates. For the carbon analyses, the selectedcovariates are joint predictors of land use and location of protected areas. Forthe poverty analyses, the covariates are joint predictors of poverty and lo-cation of protected areas. The specific covariates differ among the fourcountries, but common covariates include slope, distance to forest edge, anddistance to major cities. Each country analysis used the matching metricthat provides that the best postmatch covariate balance. A postmatchingadjustment was applied to control for residual bias in finite samples (59).

SI Appendix provides more details on matching covariates, matching met-rics, and balance metrics.

Analysis of Heterogeneity. To isolate the moderating effects of slope anddistance to cities on carbon storage and poverty (net of other influences), weused a semiparametric partial linear model on the matched data (60). Thistwo-stage estimator controls linearly for other covariates in the first stageand then, in the second stage, estimates the outcome as a nonparametricfunction of the moderator of interest using nonparametric locally weightedscatterplot smoothing. More details are provided in SI Appendix.

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