Equivalent biodiversity area: A novel metric for No Net Loss success in Brazil's changing biomes.

This study presents a new method to incorporate the No Net Loss (NNL) principle within corporate Environmental, Social, and Governance (ESG) frameworks. This principle aims to ensure that biodiversity losses from human activities are fully offset. In this context, we tackle two main challenges: managing epistemic uncertainties in environmental modeling and accurately assessing compensatory areas needed to replace lost habitats. Focusing on Brazil's diverse biomes, which are undergoing rapid changes, we highlight the role of expert opinion surveys in addressing the uncertainties of the InVEST Habitat Quality, a model that simulates changes in landscape integrity under different land use scenarios. Our analysis across three of Brazil's regions - Caatinga Semi-arid, Cerrado Savanna, and Atlantic Forest - leverages open-source data to reveal substantial habitat losses due to activities like wind farm development, mining, and intensive agriculture, leading to a widespread decline in habitat quality. We introduce the Equivalent Biodiversity Area (EBA) metric to support NNL and Net Gain of Biodiversity efforts, measured in hectares. Findings show a reduction in EBA across all studied areas, highlighting the need for effective compensation strategies. Such strategies should merge Legal Reserves and ecological restoration into ESG policies, encourage landholder collaboration, and align with larger environmental efforts, such as watershed revitalization and Biodiversity Credits markets.

Welfare Cost of Model Uncertainty in a Small Open Economy.

This paper extends the canonical small open-economy real-business-cycle model, when considering model uncertainty. Domestic households have multiplier preferences, which leads them to take robust decisions in response to possible model misspecification for the economy's aggregate productivity. Using perturbation methods, the paper extends the literature on real business cycle models by deriving a closed-form solution for the combined welfare effect of the two sources of uncertainty, namely risk and model uncertainty. While classical risk has an ambiguous effect on welfare, the addition of model uncertainty is unambiguously welfare-deteriorating. Hence, the overall effect of uncertainty on welfare is ambiguous, depending on consumers preferences and model parameters. The paper provides numerical results for the welfare effects of uncertainty measured by units of consumption equivalence. At moderate (high) levels of risk aversion, the effect of risk on household welfare is positive (negative). The addition of model uncertainty-for all levels of concern about model uncertainty and most risk aversion values-turns the overall effect of uncertainty on household welfare negative. It is important to remark that the analytical decomposition and combination of the effects of the two types of uncertainty considered here and the resulting ambiguous effect on overall welfare have not been derived in the previous literature on small open economies.

Bayesian treatment of model uncertainty for partially applicable models.

This article discusses how analyst's or expert's beliefs on the credibility and quality of models can be assessed and incorporated into the uncertainty assessment of an unknown of interest. The proposed methodology is a specialization of the Bayesian framework for the assessment of model uncertainty presented in an earlier paper. This formalism treats models as sources of information in assessing the uncertainty of an unknown, and it allows the use of predictions from multiple models as well as experimental validation data about the models' performances. In this article, the methodology is extended to incorporate additional types of information about the model, namely, subjective information in terms of credibility of the model and its applicability when it is used outside its intended domain of application. An example in the context of fire risk modeling is also provided.

Assessing the exposure of lion tamarins (Leontopithecus spp.) to future climate change.

Understanding how biodiversity will respond to climate change is a major challenge in conservation science. Climatic changes are likely to impose serious threats to many organisms, especially those with narrow distribution ranges, small populations and low dispersal capacity. Lion tamarins (Leontopithecus spp.) are endangered primates endemic to Brazilian Atlantic Forest (BAF), and all four living species are typical examples of these aggravating conditions. Here, we integrate ecological niche modeling and GIS-based information about BAF remnants and protected areas to estimate the exposure (i.e., the extent of climate change predicted to be experienced by a species) of current suitable habitats to climate change for 2050 and 2080, and to evaluate the efficacy of existing reserves to protect climatically suitable areas. Niche models were built using Maxent and then projected onto seven global circulation models derived from the A1B climatic scenario. According to our projections, the occurrence area of L. caissara will be little exposed to climate change. Western populations of L. chrysomelas could be potentially exposed, while climatically suitable habitats will be maintained only in part of the eastern region. Protected areas that presently harbor large populations of L. chrysopygus and L. rosalia will not retain climatic suitability by 2080. Monitoring trends of exposed populations and protecting areas predicted to hold suitable conditions should be prioritized. Given the potential exposure of key lion tamarin populations, we stress the importance of conducting additional studies to assess other aspects of their vulnerability (i.e., sensitivity to climate and adaptive capacity) and, therefore, to provide a more solid framework for future management decisions in the context of climate change.