Nikolas

Our understanding of deforestation is characterized by various spillover effects, but data on the network structures behind them is elusive. As a result, empirical analyses rely on proxies and strong assumptions that distort inference. In this paper, I investigate the role of spillovers in deforestation, and develop a hierarchical approach to jointly model both spillovers and the latent networks behind them. By flexibly leveraging information where available and imposing structure where needed, this approach yields insightful estimates that rely less on restrictive assumptions. I apply my approach to investigate spillovers of deforestation interventions in the Brazilian Amazon. I find that conventional estimates of spillover effects, which assume that the network structure is known, are biased downwards. Spillovers from protected areas, for instance, occur far beyond immediate neighbors, almost offsetting their positive impacts. I show how my joint model of spillovers and network structures can be used to recover more accurate and detailed insights into the nature of spillovers, e.g., by revealing particularly influential regions.

The sensitivity of econometric results is central to their credibility. In this paper, we investigate the sensitivity of regression-based inference to influential sets of observations and show how to reliably identify and interpret them. We explore three algorithmic approaches to analyze influential sets, and assess the sensitivity of a number of earlier studies in the field of development economics to them. Many results hinge on small influential sets, and inspecting them can provide crucial insights. The analysis of influential sets may reveal omitted variable bias, unobserved heterogeneity, lacking external validity, and informs about technical limitations of the methodological approach used.

Demand for agricultural products is a major driver of deforestation in the Brazilian Amazon. However, the extent of their deforestation impact is contested, as deforested land is relatively unproductive, and many products are barred from agriculture supply chains. In this paper, we quantify the deforestation impacts of expanding agricultural production, differentiating it from other channels with different implications for economic and environmental policy. We use a shift-share design, exploiting international changes in beef consumption to causally identify the deforestation impact of agricultural demand. We find that pasture and cattle herd expansions are major direct drivers of deforestation. Their direct impacts diminished during the recent deforestation boom, suggesting that land speculation motives have become more important. Our findings indicate that intensification and improved land tenure security could help decrease land pressure, but also highlight that deforestation interventions need to target the dominant role of agricultural production.

Brazil once set the example for curtailing deforestation with command and control policies, but, in the last decade, these interventions have gone astray. Environmental research and policy today are largely informed by the earlier successes of deforestation interventions, but not their recent failures. Here, we investigate the resilience of deforestation interventions. We discuss how the recent trend reversal in Brazil came to be, and what its implications for the design of future policies are. We use newly compiled information on environmental fines in an econometric model to show that the enforcement of environmental policy has become ineffective in recent years. Our results add empirical evidence to earlier studies documenting the erosion of the institutions responsible for forest protection, and highlight the considerable deforestation impacts of this erosion. Future efforts for sustainable forest protection should be aimed at strengthening institutions, spreading responsibilities, and redistributing the common value of forests via incentive-based systems.

Growing demand for minerals continues to drive deforestation worldwide. Tropical forests are particularly vulnerable to the environmental impacts of mining and mineral processing. Many local- to regional-scale studies document extensive, long-lasting impacts of mining on biodiversity and ecosystem services. However, the full scope of deforestation induced by industrial mining across the tropics is yet unknown. Here, we present a biome-wide assessment to show where industrial mine expansion has caused the most deforestation from 2000 to 2019. We find that 3,264 km2 of forest was directly lost due to industrial mining, with 80% occurring in only four countries: Indonesia, Brazil, Ghana, and Suriname. Additionally, controlling for other nonmining determinants of deforestation, we find that mining caused indirect forest loss in two-thirds of the investigated countries. Our results illustrate significant yet unevenly distributed and often unmanaged impacts on these biodiverse ecosystems. Impact assessments and mitigation plans of industrial mining activities must address direct and indirect impacts to support conservation of the world's tropical forests.

Bayesian approaches play an important role in the development of new spatial econometric methods, but are uncommon in applied work. This is partly due to a lack of accessible, flexible software for the Bayesian estimation of spatial models. Established probabilistic software struggles with the specifics of spatial econometrics, while classical implementations do not harness the flexibility of Bayesian modelling. In this paper, I present a layered, objected-oriented software architecture that bridges this gap. An R implementation in the bsreg package allows quick and easy estimation of spatial econometric models, while remaining maintainable and extensible. I demonstrate the benefits of the Bayesian approach and using a well-known dataset on cigarette demand. First, I show that Bayesian posterior densities yield better insights into the uncertainty of non-linear models. Second, I find that earlier studies overestimate spillover effects for distance-based connectivities due to a scaling error, highlighting the need for tried and tested software.

Vector autoregression (VAR) models are widely used for multivariate time series analysis in macroeconomics, finance, and related fields. Bayesian methods are often employed to deal with their dense parameterization, imposing structure on model coefficients via prior information. The optimal choice of the degree of informativeness implied by these priors is subject of much debate and can be approached via hierarchical modeling. This paper introduces BVAR, an R package dedicated to the estimation of Bayesian VAR models with hierarchical prior selection. It implements functionalities and options that permit addressing a wide range of research problems, while retaining an easy-to-use and transparent interface. Features include structural analysis of impulse responses, forecasts, the most commonly used conjugate priors, as well as a framework for defining custom dummy-observation priors. BVAR makes Bayesian VAR models user-friendly and provides an accessible reference implementation.

Deforestation of the Amazon rainforest is a threat to global climate, biodiversity, and many other ecosystem services. In order to address this threat, an understanding of the drivers of deforestation processes is required. Spillover effects and factors that differ across locations and over time play important roles in these processes. They are largely disregarded in applied research and thus in the design of evidence-based policies. In this study, we model connectivity between regions and consider heterogeneous effects to gain more accurate quantitative insights into the inherent complexity of deforestation. We investigate the impacts of agriculture in Mato Grosso, Brazil, for the period 2006–2017 considering spatial spillovers and varying impacts over time and space. Spillovers between municipalities that emanate from croplands in the Amazon appear as the major driver of deforestation, with no direct effects from agriculture in recent years. This suggests a moderate success of the Soy Moratorium and Cattle Agreements, but highlights their inability to address indirect effects. We find that the neglect of the spatial dimension and the assumption of homogeneous impacts lead to distorted inference. Researchers need to be aware of the complex and dynamic processes behind deforestation, in order to facilitate effective policy design.

In a recent study, Chaves et al. find international consumption and trade to be major drivers of ‘malaria risk’ via deforestation. Their analysis is based on a counterfactual ‘malaria risk’ footprint, defined as the number of malaria cases in absence of two malaria interventions, which is constructed using linear regression. In this letter, I argue that their study hinges on an obscured weighting scheme and suffers from methodological flaws, such as disregard for sources of bias. When addressed properly, these issues nullify results, overturning the significance and reversing the direction of the claimed relationship. Nonetheless, I see great potential in the mixed methods approach and conclude with recommendations for future studies.

Harvested biomass is linked to final consumption by networks of processes and actors that convert and distribute food and nonfood goods. Achieving a sustainable resource metabolism of the economy is an overarching challenge which manifests itself in a number of the UN Sustainable Development Goals. Modeling the physical dimensions of biomass conversion and distribution networks is essential to understanding the characteristics, drivers, and dynamics of the socio-economic biomass metabolism. In this paper, we present the Food and Agriculture Biomass Input–Output model (FABIO), a set of multiregional supply, use and input–output tables in physical units, that document the complex flows of agricultural and food products in the global economy. The model assembles FAOSTAT statistics reporting crop production, trade, and utilization in physical units, supplemented by data on technical and metabolic conversion efficiencies, into a consistent, balanced, input–output framework. FABIO covers 191 countries and 130 agriculture, food and forestry products from 1986 to 2013. The physical supply use tables offered by FABIO provide a comprehensive, transparent, and flexible structure for organizing data representing flows of materials within metabolic networks. They allow tracing of biomass flows and embodied environmental pressures along global supply chains at an unprecedented level of product and country detail and can help to answer a range of questions regarding environment, agriculture, and trade. Here we apply FABIO to the case of cropland footprints and show the evolution of consumption-based cropland demand in China, the E.U., and the U.S.A. for plant-based and livestock-based food and nonfood products.

Malaria places a great burden on the health and prosperity of many and occupies a great number of scientists and policymakers. The dynamics of the disease are tightly interwoven with economics — incidence is both tied to economic circumstances and impacts them. Economic research plays an important role in understanding and supporting the fight against malaria. The economic literature, however, features a number of peculiarities that can hamper accessibility and has been slow to approach interdisciplinary issues. In this chapter, we explain the economic perspective and summarise the literature on the economic impacts of malaria. Malaria has severe impacts on individual and aggregate economic outcomes, including mortality and morbidity, but also indirect burdens that materialise with a delay. The fight against malaria is not an economic policy per se, but may provide beneficial economic spillovers and can be vital in establishing an environment that allows for prosperity. Economic insights can make a difference in the design and implementation of effective and efficient eradication and control strategies. This is critical in the light of increasing disease (re-)exposure due to climate change and the emergence of resistant vectors and pathogens.