Overstated carbon emission reductions from voluntary REDD+ projects in the Brazilian Amazon.

Reducing emissions from deforestation and forest degradation (REDD+) has gained international attention over the past decade, as manifested in both United Nations policy discussions and hundreds of voluntary projects launched to earn carbon-offset credits. There are ongoing discussions about whether and how projects should be integrated into national climate change mitigation efforts under the Paris Agreement. One consideration is whether these projects have generated additional impacts over and above national policies and other measures. To help inform these discussions, we compare the crediting baselines established ex-ante by voluntary REDD+ projects in the Brazilian Amazon to counterfactuals constructed ex-post based on the quasi-experimental synthetic control method. We find that the crediting baselines assume consistently higher deforestation than counterfactual forest loss in synthetic control sites. This gap is partially due to decreased deforestation in the Brazilian Amazon during the early implementation phase of the REDD+ projects considered here. This suggests that forest carbon finance must strike a balance between controlling conservation investment risk and ensuring the environmental integrity of carbon emission offsets. Relatedly, our results point to the need to better align project- and national-level carbon accounting.

Evidence-Based Causal Chains for Linking Health, Development, and Conservation Actions.

Sustainability challenges for nature and people are complex and interconnected, such that effective solutions require approaches and a common theory of change that bridge disparate disciplines and sectors. Causal chains offer promising approaches to achieving an integrated understanding of how actions affect ecosystems, the goods and services they provide, and ultimately, human well-being. Although causal chains and their variants are common tools across disciplines, their use remains highly inconsistent, limiting their ability to support and create a shared evidence base for joint actions. In this article, we present the foundational concepts and guidance of causal chains linking disciplines and sectors that do not often intersect to elucidate the effects of actions on ecosystems and society. We further discuss considerations for establishing and implementing causal chains, including nonlinearity, trade-offs and synergies, heterogeneity, scale, and confounding factors. Finally, we highlight the science, practice, and policy implications of causal chains to address real-world linked human-nature challenges.

Public health impacts of ecosystem change in the Brazilian Amazon.

The claim that nature delivers health benefits rests on a thin empirical evidence base. Even less evidence exists on how specific conservation policies affect multiple health outcomes. We address these gaps in knowledge by combining municipal-level panel data on diseases, public health services, climatic factors, demographics, conservation policies, and other drivers of land-use change in the Brazilian Amazon. To fully exploit this dataset, we estimate random-effects and quantile regression models of disease incidence. We find that malaria, acute respiratory infection (ARI), and diarrhea incidence are significantly and negatively correlated with the area under strict environmental protection. Results vary by disease for other types of protected areas (PAs), roads, and mining. The relationships between diseases and land-use change drivers also vary by quantile of the disease distribution. Conservation scenarios based on estimated regression results suggest that malaria, ARI, and diarrhea incidence would be reduced by expanding strict PAs, and malaria could be further reduced by restricting roads and mining. Although these relationships are complex, we conclude that interventions to preserve natural capital can deliver cobenefits by also increasing human (health) capital.

Building the evidence base for REDD+: Study design and methods for evaluating the impacts of conservation interventions on local well-being.

Climate change mitigation in developing countries is increasingly expected to generate co-benefits that help meet sustainable development goals. This has been an expectation and a hotly contested issue in REDD+ (reducing emissions from deforestation and forest degradation) since its inception. While the core purpose of REDD+ is to reduce carbon emissions, its legitimacy and success also depend on its impacts on local well-being. To effectively safeguard against negative impacts, we need to know whether and which well-being outcomes can be attributed to REDD+. Yet, distinguishing the effects of choosing particular areas for REDD+ from the effects of the interventions themselves remains a challenge. The Global Comparative Study (GCS) on REDD+ employed a quasi-experimental before-after-control-intervention (BACI) study design to address this challenge and evaluate the impacts of 16 REDD+ pilots across the tropics. We find that the GCS approach allows identification of control groups that represent the counterfactual, thereby permitting attribution of outcomes to REDD+. The GCS experience belies many of the common critiques of the BACI design, especially concerns about collecting baseline data on control groups. Our findings encourage and validate the early planning and up-front investments required to evaluate the local impacts of global climate change mitigation efforts with confidence. The stakes are high, both for the global environment and for local populations directly affected by those efforts. The standards for evidence should be concomitantly high.

Action needed to make carbon offsets from forest conservation work for climate change mitigation.

Carbon offsets from voluntary avoided-deforestation projects are generated on the basis of performance in relation to ex ante deforestation baselines. We examined the effects of 26 such project sites in six countries on three continents using synthetic control methods for causal inference. We found that most projects have not significantly reduced deforestation. For projects that did, reductions were substantially lower than claimed. This reflects differences between the project ex ante baselines and ex post counterfactuals according to observed deforestation in control areas. Methodologies used to construct deforestation baselines for carbon offset interventions need urgent revisions to correctly attribute reduced deforestation to the projects, thus maintaining both incentives for forest conservation and the integrity of global carbon accounting.

Correction: What Is a "Community Perception" of REDD+? A Systematic Review of How Perceptions of REDD+ Have Been Elicited and Reported in the Literature.

[This corrects the article DOI: 10.1371/journal.pone.0155636.].

What Is a "Community Perception" of REDD+? A Systematic Review of How Perceptions of REDD+ Have Been Elicited and Reported in the Literature.

Reducing emissions from deforestation and forest degradation (REDD+) is expected to generate co-benefits and safeguard the interests of people who live in the forested regions where emissions are reduced. Participatory measurement, reporting and verification (PMRV) is one way to ensure that the interests of local people are represented in REDD+. In order to design and use PMRV systems to monitor co-benefits and safeguards, we need to obtain input on how local people perceive REDD+. In the literature, this is widely discussed as "community perceptions of REDD+." We systematically reviewed this literature to understand how these perceptions have been assessed, focusing specifically on how individual perceptions have been sampled and aggregated into "community perceptions." Using Google Scholar, we identified 19 publications that reported community perceptions of REDD+, including perceptions of its design, implementation, impacts, relationship with land tenure, and both interest and actual participation by local people. These perceptions were elicited through surveys of probability samples of the local population and interviews with purposively selected community representatives. Many authors did not provide sufficient information on their methods to interpret the reported community perceptions. For example, there was often insufficient detail on the selection of respondents or sampling methods. Authors also reported perceptions by unquantified magnitudes (e.g., "most people", "the majority") that were difficult to assess or compare across cases. Given this situation in the scholarly literature, we expect that there are even more severe problems in the voluminous gray literature on REDD+ not indexed by Google Scholar. We suggest that readers need to be cognizant of these issues and that publication outlets should establish guidelines for better reporting, requiring information on the reference population, sampling methods, and methods used to aggregate individual responses into "community perceptions."

National-level differences in the adoption of environmental health technologies: a cross-border comparison from Benin and Togo.

Environmental health problems such as malaria, respiratory infections, diarrhoea and malnutrition pose very high burdens on the poor rural people in much of the tropics. Recent research on key interventions-the adoption and use of relatively cheap and effective environmental health technologies-has focused primarily on the influence of demand-side household-level drivers. Relatively few studies of the promotion and use of these technologies have considered the role of contextual factors such as governance, the enabling environment and national policies because of the challenges of cross-country comparisons. We exploit a natural experimental setting by comparing household adoption across the Benin-Togo national border that splits the Tamberma Valley in West Africa. Households across the border share the same culture, ethnicity, weather, physiographic features, livelihoods and infrastructure; however, they are located in countries at virtually opposite ends of the institutional spectrum of democratic elections, voice and accountability, effective governance and corruption. Binary choice models and rigorous non-parametric matching estimators confirm that households in Benin are more likely than households in Togo to plant soybeans, build improved cookstoves and purchase mosquito nets, ceteris paribus. Although we cannot identify the exact mechanism for the large and significant national-level differences in technology adoption, our findings suggest that contextual institutional factors can be more important than household characteristics for technology adoption.

Estimating the Impacts of Local Policy Innovation: The Synthetic Control Method Applied to Tropical Deforestation.

Quasi-experimental methods increasingly are used to evaluate the impacts of conservation interventions by generating credible estimates of counterfactual baselines. These methods generally require large samples for statistical comparisons, presenting a challenge for evaluating innovative policies implemented within a few pioneering jurisdictions. Single jurisdictions often are studied using comparative methods, which rely on analysts' selection of best case comparisons. The synthetic control method (SCM) offers one systematic and transparent way to select cases for comparison, from a sizeable pool, by focusing upon similarity in outcomes before the intervention. We explain SCM, then apply it to one local initiative to limit deforestation in the Brazilian Amazon. The municipality of Paragominas launched a multi-pronged local initiative in 2008 to maintain low deforestation while restoring economic production. This was a response to having been placed, due to high deforestation, on a federal "blacklist" that increased enforcement of forest regulations and restricted access to credit and output markets. The local initiative included mapping and monitoring of rural land plus promotion of economic alternatives compatible with low deforestation. The key motivation for the program may have been to reduce the costs of blacklisting. However its stated purpose was to limit deforestation, and thus we apply SCM to estimate what deforestation would have been in a (counterfactual) scenario of no local initiative. We obtain a plausible estimate, in that deforestation patterns before the intervention were similar in Paragominas and the synthetic control, which suggests that after several years, the initiative did lower deforestation (significantly below the synthetic control in 2012). This demonstrates that SCM can yield helpful land-use counterfactuals for single units, with opportunities to integrate local and expert knowledge and to test innovations and permutations on policies that are implemented in just a few locations.

Under-mining health: environmental justice and mining in India.

Despite the potential for economic growth, extractive mineral industries can impose negative health externalities in mining communities. We estimate the size of these externalities by combining household interviews with mine location and estimating statistical functions of respiratory illness and malaria among villagers living along a gradient of proximity to iron-ore mines in rural India. Two-stage regression modeling with cluster corrections suggests that villagers living closer to mines had higher respiratory illness and malaria-related workday loss, but the evidence for mine workers is mixed. These findings contribute to the thin empirical literature on environmental justice and public health in developing countries.