The effectiveness of Payments for Ecosystem Services at delivering improvements in water quality: lessons for experiments at the landscape scale

Redesigning payments for ecosystem services to increase cost-effectiveness

Payments for Ecosystem Services (PES) are a widely used approach to incentivize conservation efforts such as avoided deforestation. Although PES effectiveness has received significant scholarly attention, how PES design modifications can improve program outcomes is less explored. We present findings from a randomized trial in Mexico that tested whether a PES contract that requires enrollees to enroll all of their forest is more effective than the traditional PES contract that allows them to choose which forest parcels to enroll. The modification's aim is to prevent landowners from enrolling only parcels they planned to conserve anyway while leaving aside other parcels to deforest. We find that the full-enrollment treatment significantly reduces deforestation compared to the traditional contract (41% less deforestation; p = 0.01). As a result, cost-effectiveness of the PES program quadruples. This result highlights the potential to substantially improve the efficacy of conservation payments through simple contract modifications.

Estimating the effectiveness of marked sidewalks: An application of the spatial causality approach

Various safety enhancements and policies have been proposed to enhance pedestrian safety and minimize vehicle-pedestrian accidents. A relatively recent approach involves marked sidewalks delineated by painted pathways, particularly in Asia's crowded urban centers, offering a cost-effective and space-efficient alternative to traditional paved sidewalks. While this measure has garnered interest, few studies have rigorously evaluated its effectiveness. Current before-after studies often use correlation-based approaches like regression, lacking effective consideration of causal relationships and confounding variables. Moreover, spatial heterogeneity in crash data is frequently overlooked during causal inference analyses, potentially leading to inaccurate estimations. This study introduces a geographically weighted difference-in-difference (GWDID) method to address these gaps and estimate the safety impact of marked sidewalks. This approach considers spatial heterogeneity within the dataset in the spatial causal inference framework, providing a more nuanced understanding of the intervention's effects. The simplicity of the modeling process makes it applicable to various study designs relying solely on pre- and post-exposure outcome measurements. Conventional DIDs and Spatial Lag-DID models were used for comparison. The dataset we utilized included a total of 13,641 pedestrian crashes across Taipei City, Taiwan. Then the crash point data was transformed into continuous probability values to determine the crash risk on each road segment using network kernel density estimation (NKDE). The treatment group comprised 1,407 road segments with marked sidewalks, while the control group comprised 3,097 segments with similar road widths. The pre-development program period was in 2017, and the post-development period was in 2020. Results showed that the GWDID model outperformed the spatial lag DID and traditional DID models. As a local causality model, it illustrated spatial heterogeneity in installing marked sidewalks. The program significantly reduced pedestrian crash risk in 43% of the total road segments in the treatment group. The coefficient distribution map revealed a range from -22.327 to 2.600, with over 95% of the area yielding negative values, indicating reduced crash risk after installing marked sidewalks. Notably, the impact of crash risk reduction increased from rural to urban areas, emphasizing the importance of considering spatial heterogeneity in transportation safety policy assessments.

Payments for ecosystem services programs: A global review of contributions towards sustainability

Payments for ecosystem services programs (PESPs) are increasingly being adopted globally to enhance sustainability outcomes. There are also hundreds of studies yearly on various aspects of PESPs, but research on their contributions to sustainability of communities and the ecosystems they depend upon at the global scale are rare. Our global review explores twelve key characteristics of PESPs at three different phases (inputs - implementation - outputs and outcomes) and their relationship types of these characteristics to sustainability outcomes. To do so, we review 376 peer-review journal articles on PESPs, and test three hypotheses related to these relationships. Our findings confirm that the relationships between each of these characteristics and sustainability outcomes are bidirectional and/or multidirectional to some extent and can be positive, negative or both, depending on specific cases and research methods used to study these relationships. The findings also disclose that separating one characteristic as the primary causal factor in any relationship or outcome is not easy as relevant characteristics are linked in a complex network. Thereby, determining key characteristics of PESPs that drive relationships for the sake of sustainability is important. Through analyzing relationships between PESP characteristics, this study offers a series of suggestions to further aid the contributions of PESPs' contributions to sustainability in the future.

The Effects of Payment for Environmental Services on Environmental Improvement and Poverty Reduction: A Meta-Regression Analysis

Based on the empirical literature of the payments for environmental services (PES) projects for the environmental improvement and poverty reduction, this paper uses a meta-regression analysis method to study whether PES can achieve the effects of improving environmental quality and reducing poverty levels. For the meta-regression analysis, the literature was mainly searched by the Web of Science core journal database. The explanatory variables of meta-regression analysis are the hypothesis and related features of the models in the original literature by generally taking the binary dummy variables or categorical variables. The dependent variables used in this paper are environmental improvement and poverty reduction. The results show that PES projects based on private ownership rights are more conducive to poverty reduction and environmental improvement. If the country is a buyer of a PES project, it is more likely to achieve the effects of environmental enhancement and poverty alleviation. PES projects based on local conditions can also achieve a win–win situation for the two effects. The effects are more effective as policy implementation time is extended. The findings suggest that in developing countries, the government should set different PES projects according to different regions conditions, to promote project implementation through individual voluntary participation, and to achieve the dual purpose of environmental governance and poverty alleviation based on the expectation of long-term implementation of participant projects.

Spatial optimization of the water quality monitoring network in São Paulo State (Brazil) to improve sampling efficiency and reduce bias in a developing sub-tropical region

Water quality monitoring networks (WQMNs) are essential to provide good data for management decisions. Nevertheless, some WQMNs may not appropriately reflect the conditions of the water bodies and their temporal/spatial dimensions, more particularly in developing countries. Also, some WQMNs may use more resources to attain management goals than necessary and can be improved. Here we analyzed the São Paulo State (Brazil) WQMN design in order to evaluate and increase its spatial representativeness based on cluster analysis and stratified sampling strategy focused on clear monitoring goals. We selected water resources management units (UGRHIs) representative of contrasting land uses in the state, with bimonthly data from 2004 to 2018 in 160 river/stream sites. Cluster analysis indicated monitoring site redundancy above 20% in most of the UGRHIs. We identified heterogeneous spatial strata based on land use, hydrological, and geological features through a stratified sampling strategy. We identified that monitoring sites overrepresented more impacted areas. Thus, the network is biased against determination of baseline conditions and towards highly modified aquatic systems. Our proposed spatial strategy suggested the reduction of the number of sites up to 12% in the UGRHIs with the highest population densities, while others would need expansions based on their environmental heterogeneity. The final densities ranged from 1.6 to 13.4 sites/1,000km². Our results illustrate a successful approach to be considered in the São Paulo WQMN strategy, as well as providing a methodology that can be broadly applied in other developing countries.

Subjective modeling choices and the robustness of impact evaluations in conservation science

Arbitrary modeling choices are inevitable in scientific studies. Yet, few empirical studies in conservation science report the effects these arbitrary choices have on estimated results. I explored the effects of subjective modeling choices in the context of counterfactual impact evaluations. Over 5000 candidate models based on reasonable changes in the choice of statistical matching algorithms (e.g., genetic and nearest distance mahalanobis matching), the parametrization of these algorithms (e.g., number of matches), and the inclusion of specific covariates (e.g., distance to nearest city, slope, or rainfall) were valid for studying the effect of Virunga National Park in Democratic Republic of the Congo on changes in tree cover loss and carbon storage over time. I randomly picked 2000 of the 5000 candidate models to determine how much and which subjective modeling choices affected the results the most. All valid models indicated that tree cover loss decreased and carbon storage increased in Virunga National Park from 2000 to 2019. Nonetheless, the order of magnitude of the estimates varied by a factor of 3 (from -4.78 to -13.12 percentage points decrease in tree cover loss and from 20 to 46 t Ce/ha for carbon storage). My results highlight that modeling choices, notably the choice of the matching algorithm, can have significant effects on point estimates and suggest that more structured robustness checks are a key step toward more credible findings in conservation science.

Evaluating Impact Using Time-Series Data

Humanity's impact on the environment is increasing, as are strategies to conserve biodiversity, but a lack of understanding about how interventions affect ecological and conservation outcomes hampers decision-making. Time series are often used to assess impacts, but ecologists tend to compare average values from before to after an impact; overlooking the potential for the intervention to elicit a change in trend. Without methods that allow for a range of responses, erroneous conclusions can be drawn, especially for large, multi-time-series datasets, which are increasingly available. Drawing on literature in other disciplines and pioneering work in ecology, we present a standardised framework to robustly assesses how interventions, like natural disasters or conservation policies, affect ecological time series.

Mechanisms and impacts of an incentive-based conservation program with evidence from a randomized control trial

Conservation science needs more high-quality impact evaluations, especially ones that explore mechanisms of success or failure. Randomized control trials (RCTs) provide particularly robust evidence of the effectiveness of interventions (although they have been criticized as reductionist and unable to provide insights into mechanisms), but there have been few such experiments investigating conservation at the landscape scale. We explored the impact of Watershared, an incentive-based conservation program in the Bolivian Andes, with one of the few RCTs of landscape-scale conservation in existence. There is strong interest in such incentive-based conservation approaches as some argue they can avoid negative social impacts sometimes associated with protected areas. We focused on social and environmental outcomes based on responses from a household survey in 129 communities randomly allocated to control or treatment (conducted both at the baseline in 2010 and repeated in 2015-2016). We controlled for incomplete program uptake by combining standard RCT analysis with matching methods and investigated mechanisms by exploring intermediate and ultimate outcomes according to the underlying theory of change. Previous analyses, focused on single biophysical outcomes, showed that over its first 5 years Watershared did not slow deforestation or improve water quality at the landscape scale. We found that Watershared influenced some outcomes measured using the survey, but the effects were complex, and some were unexpected. We thus demonstrated how RCTs can provide insights into the pathways of impact, as well as whether an intervention has impact. This paper, one of the first registered reports in conservation science, demonstrates how preregistration can help make complex research designs more transparent, avoid cherry picking, and reduce publication bias.

Barnes M, Geldmann J, Oldekop JA, Jones JPG. Statistical matching for conservation science

The awareness of the need for robust impact evaluations in conservation is growing and statistical matching techniques are increasingly being used to assess the impacts of conservation interventions. Used appropriately matching approaches are powerful tools, but they also pose potential pitfalls. We outlined important considerations and best practice when using matching in conservation science. We identified 3 steps in a matching analysis. First, develop a clear theory of change to inform selection of treatment and controls and that accounts for real-world complexities and potential spillover effects. Second, select the appropriate covariates and matching approach. Third, assess the quality of the matching by carrying out a series of checks. The second and third steps can be repeated and should be finalized before outcomes are explored. Future conservation impact evaluations could be improved by increased planning of evaluations alongside the intervention, better integration of qualitative methods, considering spillover effects at larger spatial scales, and more publication of preanalysis plans. Implementing these improvements will require more serious engagement of conservation scientists, practitioners, and funders to mainstream robust impact evaluations into conservation. We hope this article will improve the quality of evaluations and help direct future research to continue to improve the approaches on offer.

What role should randomized control trials play in providing the evidence base for conservation?

The effectiveness of many widely used conservation interventions is poorly understood because of a lack of high-quality impact evaluations. Randomized control trials (RCTs), in which experimental units are randomly allocated to treatment or control groups, offer an intuitive way to calculate the impact of an intervention by establishing a reliable counterfactual scenario. As many conservation interventions depend on changing people's behaviour, conservation impact evaluation can learn a great deal from RCTs in fields such as development economics, where RCTs have become widely used but are controversial. We build on relevant literature from other fields to discuss how RCTs, despite their potential, are just one of a number of ways to evaluate impact, are not feasible in all circumstances, and how factors such as spillover between units and behavioural effects must be considered in their design. We offer guidance and a set of criteria for deciding when RCTs may be an appropriate approach for evaluating conservation interventions, and factors to consider to ensure an RCT is of high quality. We illustrate this with examples from one of the few concluded RCTs of a large-scale conservation intervention: an incentive-based conservation programme in the Bolivian Andes. We argue that conservation should aim to avoid a rerun of the polarized debate surrounding the use of RCTs in other fields. Randomized control trials will not be feasible or appropriate in many circumstances, but if used carefully they can be useful and could become a more widely used tool for the evaluation of conservation impact.

Evidence Types and Trends in Tropical Forest Conservation Literature

To improve the likelihood of conservation success, donors, policy makers, nongovernmental organizations (NGOs), and researchers are increasingly interested in making conservation decisions based on scientific evidence. A major challenge in doing so has been the wide variability in the methodological rigor of existing studies. We present a simple framework to classify different types of conservation evidence, which can be used to understand the strengths, weaknesses, and biases in the conservation effectiveness literature. We then apply this framework to evaluate the evidence for the efficacy of four important strategies in tropical forest conservation. Even though there has been an increase in methodologically rigorous studies over time, countries that are globally important in terms of their biodiversity are still heavily under-represented by any type of conservation effectiveness evidence.