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An important part of conservation practice is the empirical evaluation of program and policy impacts. Understanding why conservation programs succeed or fail is essential for designing cost‐effective initiatives and for improving the livelihoods of natural resource users. The evidence we seek can be generated with modern impact evaluation designs. Such designs measure causal effects of specific interventions by comparing outcomes with the interventions to outcomes in credible counterfactual scenarios. Good designs also identify the conditions under which the causal effect arises. Despite a critical need for empirical evidence, conservation science has been slow to adopt these impact evaluation designs. We identify reasons for the slow rate of adoption and provide suggestions for mainstreaming impact evaluation in nature conservation.

It has been suggested that rainfall in the Amazon decreases if forest loss exceeds some threshold, but the specific value of this threshold remains uncertain. Here, we investigate the relationship between historical deforestation and rainfall at different geographical scales across the Southern Brazilian Amazon (SBA). We also assess impacts of deforestation policy scenarios on the region’s agriculture. Forest loss of up to 55–60% within 28 km grid cells enhances rainfall, but further deforestation reduces rainfall precipitously. This threshold is lower at larger scales (45–50% at 56 km and 25–30% at 112 km grid cells), while rainfall decreases linearly within 224 km grid cells. Widespread deforestation results in a hydrological and economic negative-sum game, because lower rainfall and agricultural productivity at larger scales outdo local gains. Under a weak governance scenario, SBA may lose 56% of its forests by 2050. Reducing deforestation prevents agricultural losses in SBA up to US$ 1 billion annually. Deforestation in the Amazon region has suggested to influence precipitation in a non-linear way. Here, the authors show that forest loss is associated with decreasing precipitation after a scale-dependent threshold is crossed, which can cause stress on agriculture if deforestation is expanded.

The PLOS ONE Collection \"Measuring forest conservation effectiveness\" brings together a series of studies that evaluate the effectiveness of tropical forest conservation policies and programs with the goal of measuring conservation success and associated co-benefits. This overview piece describes the geographic and methodological scope of these studies, as well as the policy instruments covered in the Collection as of June 2016. Focusing on forest cover change, we systematically compare the conservation effects estimated by the studies and discuss them in the light of previous findings in the literature. Nine studies estimated that annual conservation impacts on forest cover were below one percent, with two exceptions in Mexico and Indonesia. Differences in effect sizes are not only driven by the choice of conservation measures. One key lesson from the studies is the need to move beyond the current scientific focus of estimating average effects of undifferentiated conservation programs. The specific elements of the program design and the implementation context are equally important factors for understanding the effectiveness of conservation programs. Particularly critical will be a better understanding of the causal mechanisms through which conservation programs have impacts. To achieve this understanding we need advances in both theory and methods.

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.

Land use and land cover change affects two climate-relevant landscape dimensions: land cover composition and its spatial configuration. While the climate impacts of changes in land cover composition are well documented, the role of changes in land cover configuration remains poorly understood. Here, we examine how configuration changes in cropland-forest landscapes affect the ecosystem´s climate-regulating functions by conducting a multi-scenario experiment. We use the Terrestrial System Modeling Platform (TSMP), a modeling framework to run coupled high-resolution regional climate models (RCMs) with a focus on terrestrial hydrology. We employ stylized landscapes as input for TSMP and combine machine learning with SHapley Additive exPlanations values, an explanatory AI technique, to disentangle direct and mediating contributions. Our results indicate that changes in land cover configuration influence topsoil and 2 m air temperatures when composition is kept constant. Greater forest fragmentation is associated with overall warming, particularly over cropland during the growing season. This can mainly be attributed to reduced wind speed, lower latent heat flux, and higher sensible heat flux. These findings support the hypothesis that increasing forest fragmentation reduces the landscape’s capacity to mitigate local climate. However, we also discuss potential biases towards warming effects in contemporary RCMs. Our study demonstrates the potential of RCMs to address key land system science questions and promote interdisciplinary research at the intersection of climate change and landscape management. The type of experiments conducted may help to leverage both modeling and observational studies to advance our knowledge about the climate-relevance of landscape characteristics and arrive at scalable adaptation strategies.

Deforestation in the tropics remains a significant global challenge linked to carbon emissions and biodiversity loss. Agriculture, forestry, wildfires, and urbanization have been repeatedly identified as main drivers of tropical deforestation. Understanding the underlying mechanisms behind these direct causes is crucial to navigate the multiple tradeoffs between competing forest uses, such as food and biomass production (SDG 2), climate action (SDG 13), and life on land (SDG 15). This paper develops and implements a global-scale empirical approach to quantify two key factors affecting land use decisions at tropical forest frontiers: agricultural commodity prices and national governance. It relies on data covering the period 2004–2015 from multiple public sources, aggregated to countries and agro-ecological zones. Our analysis confirms the persistent influence of commodity prices on agricultural land expansion, especially in forest-abundant regions. Economic and environmental governance quality co-determines processes of expansion and contraction of agricultural land in the tropics, yet at much smaller magnitudes than other drivers. We derive land supply elasticities for direct use in standard economic impact assessment models and demonstrate that our results make a difference in a Computable General Equilibrium framework.

Deforestation in the Brazilian Amazon has dropped substantially after a peak of over 27 thousand square kilometers in 2004. Starting in 2008, the Brazilian Ministry of the Environment has regularly published blacklists of critical districts with high annual forest loss. Farms in blacklisted districts face additional administrative hurdles to obtain authorization for clearing forests. In this paper we add to the existing literature on evaluating the Brazilian anti-deforestation policies by specifically quantifying the impact of blacklisting on deforestation. We first use spatial matching techniques using a set of covariates that includes official blacklisting criteria to identify control districts. We then explore the effect of blacklisting on change in deforestation in double difference regressions with panel data covering the period from 2002 to 2012. Multiple robustness checks are conducted including an analysis of potential causal mechanisms behind the success of the blacklist. We find that the blacklist has considerably reduced deforestation in the affected districts even after controlling for the potential mechanism effects of field-based enforcement, environmental registration campaigns, and rural credit.

Bioplastic production is a small but fast growing sector in the global bioeconomy, which may benefit from public support measures in the future as governments seek to promote more sustainable consumption patterns. Here we assess the potential net economy-wide impacts of a 5% bioplastic target relative to current plastic consumption in the main producing regions. We compare two alternative policy strategies to achieve the target in a general equilibrium framework that allows for substitution between conventional and bio-based plastics: a subsidy on bioplastics versus a tax on fossil-based plastic consumption. Our study is the first to quantify global greenhouse gas (GHG) emissions from an increased demand for bioplastics on a global scale, produced from arable crops, considering both direct and indirect land use change (LUC). The tax provokes a contraction of all sectors that employ plastics, which leads to a drop of 0.07% in global real GDP, whereas the subsidy has no significant effect on the global economy. Both tax and subsidy reduce world demand for petroleum products, by 0.37% and 0.07%, respectively, boosting demand for sugar- and starch-based feedstock in the bioplastic industry. This leads to emissions from LUC globally, which correspond to a carbon payback time of 22 years on average, with the associated annual abatement costs of over US$2000 per tonne of CO2-eq. The tax has greater GHG reduction potential in bioplastic producing regions but generates greater economic and environmental spillover effects in countries that do not enforce the target. Results show that promoting bioplastic consumption is not a cost-effective strategy for climate change mitigation if based on conventional feedstock, due to market-mediated GHG emissions from LUC. Bioplastics are not necessarily more sustainable than conventional polymers just because they are bio-based, although further assessment of potential environmental gains associated with biodegradability and recyclability is desirable.

The Brazilian Amazon and Cerrado biomes have been subject to strong pressure from agricultural expansion over the past two decades. A common claim is that the associated tree cover loss was partly driven by speculative land acquisition. In this paper, we analyze the effects of information on planned road infrastructure improvements and changes in conservation policy implementation on expectations of forest conversion. We use a unique land price dataset covering the period from 2001-2012. Based on land rent and hedonic valuation theory, we argue that forestland prices convey information on expected future land use. We decompose forestland prices into a conventional forestland rent and a speculative part related to forestland conversion and alternative land use rents. Using a fixed-effect panel, we then assess whether, where, and to what extent changes in conservation policy affect forestland prices over time. Our results confirm that forestland prices contain expectations about converting forestland to agricultural or pasture land. We also find indications that the Brazilian land market conveys information about potential conservation policy leakage and explore this conjecture descriptively using dynamic deforestation hotspot maps.

Annual forest loss in the Brazilian Amazon had in 2012 declined to less than 5,000 sqkm, from over 27,000 in 2004. Mounting empirical evidence suggests that changes in Brazilian law enforcement strategy and the related governance system may account for a large share of the overall success in curbing deforestation rates. At the same time, Brazil is experimenting with alternative approaches to compensate farmers for conservation actions through economic incentives, such as payments for environmental services, at various administrative levels. We develop a spatially explicit simulation model for deforestation decisions in response to policy incentives and disincentives. The model builds on elements of optimal enforcement theory and introduces the notion of imperfect payment contract enforcement in the context of avoided deforestation. We implement the simulations using official deforestation statistics and data collected from field-based forest law enforcement operations in the Amazon region. We show that a large-scale integration of payments with the existing regulatory enforcement strategy involves a tradeoff between the cost-effectiveness of forest conservation and landholder incomes. Introducing payments as a complementary policy measure increases policy implementation cost, reduces income losses for those hit hardest by law enforcement, and can provide additional income to some land users. The magnitude of the tradeoff varies in space, depending on deforestation patterns, conservation opportunity and enforcement costs. Enforcement effectiveness becomes a key determinant of efficiency in the overall policy mix.