Paying for Biodiversity: Enhancing the Cost-Effectiveness of Payments for Ecosystem Services

In the following, we choose a narrower delimitation, focused on conditionality as sine qua non PES criterion [ 4 ]. While this choice evidently caps our sample size, it allows us to look at interventions that are truly comparable. We also only include cases with one or more pre-existing academic assessments in the literature, aiming thus at assessing only PES schemes with scientifically validated sources of information.

By adopting a globally scoped systematic literature review, including all terrestrial environmental services, we reach a sample of 55 PES schemes. Compared to [ 34 ] and [ 35 ], we extend the set of design-oriented e. Compared to [ 36 ], our twice as large sample enhances the statistical analysis, and treats new research questions. We thus believe our sample is as broad as the current state of PES affairs permits, and yet generically similar to be able to compare initiatives.

This article is structured as follows. In Section 2, we describe in detail our sample and methods. In Section 3 Results , we follow a sequential approach, going from open-ended exploratory methods to an analysis with an assumed causal direction. We start with a descriptive statistics, then turn to b a categorical principal component analysis of PES variables, and lastly c analyse econometrically how PES design likely affects environmental additionality.

Finally, we discuss the scope of our findings, and their implications for future research and policies Section 4. While hundreds of PES schemes are reported loosely upon in the literature, most contributions do not provide sufficient in-depth information to be useful for quantitative meta-analysis. We selected our case studies based on different methodological guidelines for meta-analysis, derived from clinical and social sciences [ 37 — 39 ].

From the resulting identified records, we filtered out cases where payments to ES providers i could not be confirmed to have occurred at least once never mind whether the program was still ongoing ; ii did not conform well to our conditionality-focused definition for PES; or iii did not in the PES case study deliver sufficient descriptors to meet our minimum data standards.

This narrowed our sample to 90 literature references referring to a total of 55 PES cases worldwide counting until mid when our statistical analysis was begun , of which 47 are ongoing See S1 Table for meta-analysis references and Fig 1 for the PRISMA selection diagram. Fig 2 shows the geographic and thematic focus of the selected PES cases. They feature payments for the protection and restoration of watersheds Water with 22 cases, the most frequently targeted ES , for biodiversity conservation Biodiversity 10 cases , for climate change mitigation through carbon sequestration or avoided deforestation Carbon 8 cases , and for multiple services from agriculturally dominated systems Multiple-Agriculture 12 cases.

Region-wise, countries with emerging economies dominate, especially Latin America with 23 cases. Industrialised countries have less PES cases, though some are huge government schemes that outsize small-scale initiatives by orders of magnitude. We did not weight our observation in terms of scheme size, but treated each case equally. Hence, our analysis comes to put relatively more emphasis on a series of smaller-scale PES schemes that predominate in non-OECD countries. We built a database of in total 50 basic variables, a dozen of which we employ analytically. The main categories are:.

Generally, we combined categorical no hierarchy between levels , ordinal hierarchical between levels , and continuous variables cardinal ranking in our analysis.

• INTRODUCTION.
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S1 Database describes all variables in detail, but a few points are worth flagging. We registered which actors participated in different implementation stages, and classified them between public, private commercial enterprises and private non-commercial NGOs, foundations, grassroots organizations sectors iii.

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Under funding sources iv , we describe which sectors assumed which payments cash and in-kind. Costs were divided into upfront e. Schemes totally or mainly funded by the public sector were classified as public-sector schemes; correspondingly for the two other sectors. This composite indicator allows us to capture the degree of implementation closeness to PES theory, using an ordinal indicator with scores ranging from 6 to 17 S2 Table. Descriptive statistics of the main variables are shown in Table 2. Finally, environmental additionality vi was clearly the most challenging variable, but also of great interest [ 40 ].

One previous meta-analysis used expert scores [ 35 ], another implementer self-assessment [ 34 ] of the degree of environmental success. Potentially we could have focused exclusively on studies using so-called rigorous impact assessment methods. Even when researchers have not yet produced the perfect data sets for quantitative impact evaluation, practitioners and policy makers still need to make real-world choices about how to design their interventions, taking stock of the current state of affairs in the best possible way.

In this study, we have thus opted for including both quantitative and qualitative documentation on environmental additionality, and classified it according to the precision of the assessment methods, resulting in five levels S3 and S4 Tables. Consequently, we opted for the least pretentious outcome assessment: In addition, we performed various sensitivity analyses on our additionality assessments. From the total of 55 PES cases, for four simply no attempt had been made to gauge environmental additionality, thus restraining the number of cases for the additionality analysis to Our first interest is whether, as hypothesized in the PES literature, substantial differences exist between the characteristics of public vs.

Geographically, sector funding exhibits three patterns: Half of these private schemes are run by the private commercial sector, especially for eco-tourism and wildlife. Do sectors alternate their lead of PES schemes over time? For one fourth of our cases, this is the case. Where shifts occur, most frequently the public sector covers the initial costs, transferring the project afterwards to the private commercial sector to manage it.

Such sequential transfer of leadership between private and public environment sectors has also been observed in other sectors [ 22 ].

How do per-hectare payments vary across the sample? Carbon, biodiversity and multi-functional agriculture PES schemes score similar in size, although carbon PES show large variability. Multifunctional agricultural PES schemes vary the least in payment amounts, and account for large areas. The lowest payments are for biodiversity PES, but with large variance. As expected, payments vary much less in public than in private schemes see S5 Table for details. PES schemes typically exhibit differences across services and implementing sectors public vs.

In this subsection, we use multi-dimensional categorical principal component CatPCA and cluster analyses to explore underlying patterns. CatPCA reduces the multidimensional space represented by this matrix into a given number usually two or three of orthogonal independent dimensions. Each dimension is defined through a combination of the variables, and represents in decreasing order a given percentage of the total variability in the matrix.

Fig 4 shows the first two dimensions of the analysis, representing a Triangles refer to public PES, circles to private. We additionally performed a cluster analysis distance measure: Sorensen; group linkage method: The drawn border lines in Fig 4 delimit the PES schemes belonging to each of the three clusters. The combined CatPCA and cluster show which schemes have closest coordinates, and thus are more similar, the degree of concentration of PES schemes within groups, and the distance between groups.

Variables with extreme values in one or both of the two axes define the cluster characteristics, since each axis is a vector composed by the weights of all the variables. On the contrary, variables close to the origin of the quadrant lines are variables that are present in two or more groups, and therefore do not explain the differences between groups. We identify the following three groupings:. While the PES literature previously had recognized important differences between public and private PES schemes [ 9 , 17 ], our analysis here thus also points to important differences within the private group between commercial and NGO-led PES schemes, e.

Conversely, it is also interesting to observe that forest ecosystems are placed very close to the intersection of both axis, suggesting that conserving forested ecosystems through PES is being approached from a combination of agri-environmental, carbon and biodiversity schemes. As explained above, in answering this question we are handicapped by an extreme scarcity of rigorous PES impact evaluations, and by a sample size that moderately limits our degrees of freedom in statistical analysis.

In response, we attempt in this section to answer econometrically a more limited sub-question: An emerging literature on the principles of environmentally effective PES design points to particularly three critical factors of high potential [ 44 — 48 ]:. For spatial targeting i , we distinguished three progressive levels: Differentiated payments ii were classified binarily, according to whether or not more than one single payment level predominantly per-hectare, but also per-household payments was used.

Conditionality iii we constructed as the product of indices for documented monitoring and sanctioning efforts, respectively. In addition, we included three control variables in the estimation. First, the public vs. Finally, the time elapsed since project start could also be stage-setting, e. Table 3 shows the result of the binary logistical regression testing the predictive level of the above described critical design variables on environmental additionality for the 51 cases for which it was possible to obtain an estimation of additionality.

Starting with the control variables a in Model I, we see that it makes the expected difference for assessed additionality whether the PES scheme is asset-building versus preventively conserving nature by avoiding projected pressures. This expresses probably that it is much easier to verify the achievement of an additional outcome when an asset has been added e. Interestingly, the two sectoral control variables do not come out as significant, in spite of having been flagged as important determinants of clusters in the above.

We attribute this to the inclusion of design variables: Since most of the schemes are still ongoing, implementation time thus widely comes to reflect the calendar year when the scheme was implemented. While there is thus little evidence of learning-by-doing impacts within each PES scheme, implementers may indeed learn more from each other e. Turning now to the three PES design variables, they were all estimated with the expected positive sign i.

This is an important finding, which reconfirms the recommendations for implementation that are being put forward in the more theoretically orientated PES literature. Our results could potentially have been affected by the precision level of the environmental additionality estimations.

To control for the sensitivity of the model to this factor, we run the previous model including the created ordinal variable describing the precision of additionality measures Model II in Table 3. Diversification of payments, spatial targeting and conditionality. Meta-analyses can help us recognizing global empirical patterns of PES. As the PES literature flourishes and new case studies are continuously being added, we have the opportunity to gain more knowledge about commonalities in implementation and outcomes.

In this article, we took advantage of the expansion of PES case studies in recent years to construct a global database.

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We used a fairly narrow and explicit definition of PES, in order to identify comparable schemes. The cases selected show a balanced number of schemes by geographical region, ecosystem type, ecosystem service and economic sector involved, showing that the systematic review procedure succeeded in limiting the risk of selection bias. For instance, the limited number of African PES schemes included in our study reflects the actual slower up-take of PES in this continent.

We also filtered out cases without the availability of sufficient reliable documentation. We then conducted some exploratory analysis of the emerging patterns of implementation, going from purely descriptive bivariate statistics to principal component analysis of emerging variable clusters, and finally to test some theoretically sustained hypotheses regarding the association of alleged key PES design variables with positive environmental additionality. From our descriptive analysis, we noted significant differences between publicly and privately financed PES schemes e.

Public sector PES participation is high in Europe and Asia with a tradition for public-sector environmental management , yet very low in Sub-Saharan Africa, where public sector institutions have lower capacity to organize PES schemes. Latin America, the prime region of PES implementation, displays a large variety of arrangements.

Each of these interacts with a set of variables ecosystem, target ES, lead actors, etc. While we thus reconfirm the hypothesis from the pre-existing PES literature that public and private PES differ significantly, our results also point to important differences between the two types of private PES schemes: Still, also some permeability between the three groups remains, showing that PES implementation is also the result of a hybridization and cooperation between public and private sectors.

Finally, we also attempted to scrutinize which PES design factors influenced environmental outcomes, as measured by a binarily defined proxy of environmental additionality. We confirmed the significance of spatial targeting for ES density and threat and, to a statistically somewhat lesser extent, of payment differentiation as opposed to uniform payments and the degree of conditionality monitoring and sanctioning efforts applied.

Payments for building environmental assets were also more likely to be additional than payments for avoiding damages. Interestingly, the public vs. This could imply that the main differences in outcomes between public and private PES manifest themselves through divergences in technical PES design principles, rather than being sui generis differences. Our additionality findings are robust to the sensitivity analysis with respect to the precision in measuring environmental additionality: As so often in complex interactions between social and biophysical systems, could there potentially be problems of endogeneity in the relations we tested for in our binary logistical regression analysis?

For instance, additionality targets may certainly from the outset be lower in public PES schemes, which tend to have more side-objectives.

Payments for Ecosystem Services | UNDP

However, this should be controlled for by our sector variables. We are thus confident that endogeneity does not constitute a major problem, if any. Yet, as a precautionary measure and sensitivity check, we also ran the model at three different probability cut-off levels 0. We find no changes in model descriptors although the highest accuracy is obtained with the equal-probability assumption of 0.

This reinforces our conviction that our current model specification is robust. For future research, we believe our global-comparative analysis could eventually be improved by including more cases, as they become available, and by more consolidated information about rigorously evaluated key PES outcomes, in both environmental and socioeconomic terms.

Could Payments for Ecosystem Services Create an "Ecosystem Service Curse"?

However, the current scarcity of rigorous impact evaluations applies not only to PES, but basically to any conservation tool other than protected areas [ 32 , 41 ]. While our additionality measure and some of the design proxies are admittedly still rough approximations, our results can be seen as a first set of pointers, to be tested subsequently in more sophisticated ways with more and better data. Nevertheless, our analyses give an interesting indication that, after controlling for various contextual factors, the application of the best PES design principles may add value to the environmental outcomes.

This represents a call for greater efforts of using state-of-the-art principles to make PES design more sophisticated, e. As a recommendation, this will probably not be favored across the board by all PES implementers. Yet, to put it conversely, our results indicate that there may be ample efficiency costs attached to the over-simplification of policies and interventions in environmental incentive programs such as PES.

The authors would like to thank the two anonymous reviewers for their helpful comments. European Commission grant number: The ANR funded part of the scientific meetings that contributed to data analysis and the writing of the manuscript. Funding provided by 1. National Center for Biotechnology Information , U. Published online Mar 3. Author information Article notes Copyright and License information Disclaimer. The authors have declared that no competing interests exist. Received Jun 29; Accepted Feb 5. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

This article has been cited by other articles in PMC. References used for the description of PES selected schemes. Fitness to a canonical PES scheme composite indicator. Pearson correlation levels and significance between the predictor variables of the bivariate additionality model. Collinearity analysis for the predictor variables of the bivariate additionality model. Logistic regression models with cut-off level set at 0. PES schemes and descriptors. Abstract Assessing global tendencies and impacts of conditional payments for environmental services PES programs is challenging because of their heterogeneity, and scarcity of comparative studies.

Introduction Payments for environmental services PES have become an increasingly popular tool for environmental management, supplementing policy tools that were previously widely focused on command-and-control measures. Sample and Methods While hundreds of PES schemes are reported loosely upon in the literature, most contributions do not provide sufficient in-depth information to be useful for quantitative meta-analysis.

Table 1 Database search protocol for PES studies included in study sample. Database Search strategy Search terms Total references after duplicates Filtering conditions Total meta-analysis references Science direct Databases: All sources Subjects included: The article ' How to attract PES investment from private business?

Identifying sellers and target ecosystem service benefits: Accounting for spatial variation in ecosystem service benefits via economic valuation, benefit scoring, and mapping tools allows payments to be prioritised to areas that provide the highest benefits.

If the PES budget is limited, this can substantially increase the cost-effectiveness of the programme.

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Establishing baselines and target payments to ecosystem services that are at risk of loss, or to enhance their provision: A PES programme should only make payments for ecosystem services that are additional to the business-as-usual baseline. Differentiating payments based on the opportunity costs of ecosystem service provision: PES programmes that reflect the cost of an alternative action that must be avoided e.

Consider bundling or layering multiple ecosystem services: Joint provision of multiple services can provide opportunities to increase the benefits of the programme, while reducing transaction costs.

This is clearly demonstrated by the article ' Bundled' PES schemes to boost cost-effectiveness '. Leakage occurs when measures to enhance ecosystem services provision in one location leads to increased pressures for conversion in another. If leakage risk is expected to be high, the scope of the monitoring and accounting framework may need to be expanded so as to detect, and consequently address, leakage.

Events such as forest fires may undermine the ability of a landholder to provide an ecosystem service as stipulated in a PES agreement. If the risks are high, this will impede the effective functioning of a PES market. Delivering performance-based payments and ensure adequate enforcement: Payments should be ex-post, conditional on performance.

When this is not feasible, effort-based payments such as changes in management practices are a second best alternative, provided that changes in ecosystem management practices will bring about the desired change in service provision.

Payments for Ecosystem Services

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