BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R

Vector autoregression (VAR) models are widely used models for multivariate time series analysis, but often suffer from their dense parameterization. Bayesian methods are commonly employed as a remedy by imposing shrinkage on the model coefficients via informative priors, thereby reducing parameter uncertainty. The subjective choice of the informativeness of these priors is often criticized and can be alleviated via hierarchical modeling. This paper introduces BVAR, an R package dedicated to the estimation of Bayesian VAR models in a hierarchical fashion. It incorporates functionalities that permit addressing a wide range of research problems while retaining an easy-to-use and transparent interface. It features the most commonly used priors in the context of multivariate time series analysis as well as an extensive set of standard methods for analysis. Further functionalities include a framework for defining custom dummy-observation priors, the computation of impulse response functions, forecast error variance decompositions and forecasts.Series: Department of Economics Working Paper Serie

BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R

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

Unveiling Drivers of Deforestation: Evidence from the Brazilian Amazon

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. Indirect impacts and determinants that eventually differ across locations and over time are important factors in these processes. These are largely disregarded in applied research and thus in the design of evidence-based policies. In this study, we employ a flexible modelling framework to gain more accurate quantitative insights into the complexities of deforestation phenomena. We investigate the impacts of agriculture in Mato Grosso, Brazil, for the period 2006-2017 and explicitly consider spatial spillovers and varying impacts over time and space. Spillover effects from croplands in the Amazon appear as the major driver of deforestation, with no direct effects from agriculture in later years. This suggests moderate success of the Soy Moratorium and Cattle Agreements, but highlights their inability to address indirect effects. We find that neglect of spatial dynamics and the assumption of homogeneous impacts leads to distorted inference. Researchers need to be aware of the complex and dynamic processes behind deforestation, in order to facilitate effective policy design.Series: Ecological Economic Paper

FABIO - The Construction of the Food and Agriculture Biomass Input-Output Model

Primary crops are linked to final consumption by networks of processes and actors that convert and distribute food and non-food goods. Achieving a sustainable metabolism of this bio-economy is an overarching challenge which manifests itself in a number of the UN Sustainable Development Goals. Modelling the physical dimensions of biomass conversion and distribution networks is essential to understanding the characteristics, drivers and dynamics of our societies' biomass metabolism. In this paper, we present the Food and Agriculture Biomass Input-Output model (FABIO), a set of multi-regional 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 utilisation 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 organising data representing flows of materials within metabolic networks. They allow tracing 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.Series: Ecological Economic Paper

Inadequate methods undermine a study of malaria, deforestation and trade

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

A set of essential variables for modelling environmental impacts of global mining land use

This repository provides a set of essential variables to support research on forest loss driven by mining. All variables have been resampled to 30 arcsec spatial resolution (approximately 1 by 1 km at the equator) and are encoded in Geographic Tagged Image File Format (GeoTIFF). The grid extends from the longitude −180 to 180 degrees and from the latitude −90 to 90 degrees in the geographical reference system WGS84. Cells over water have no-data values. Below we describe the list of variables, sources, and processing steps.area_of_mines_circa_2018.tif: mining area in square metres. This layer was derived from a global-scale data set of mining polygons [Maus et al., 202a,b0] available from [doi:10.1594/PANGAEA.910894] under CC BY-SA 4.0 license. The mining area for each 30 arcsec grid was calculated intersecting cells and mining polygons.distance_to_mine_circa_2018.tif: distance to the nearest mine in metres. This layer was derived by calculating the Euclidean distance between each grid cell's centroid to the centroid of the closest grid cell with mine presence, i.e. cells where area_of_mines_circa_2018.tif > 0.area_of_forest_cover_circa_2000.tif: area of forest cover in square metres. This layer was derived from the Global Forest Change (GFC) dataset [Hansen et al., 2013] version 1.7 available from [https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html] under CC BY 4.0 license. We aggregated the GFC data from 1 arcsec to our 30 arcsec grid cells by summing the area of forest cover pixels weighted by their surface intersection with the 30 arcsec cells.area_of_forest_cover_within_mines_circa_2000.tif: area of forest cover in square metres. This layer was derived using the same methods as area_of_forest_cover_circa_2000.tif; however, it only includes forest area intersecting mining polygons, i.e. the on-site forest cover circa 2000.area_of_forest_cover_loss_yearly_from_2001_to_2019.tif: area of forest cover loss in square metres. This GeoTIFF file has 19 layers (one layer per year) starting from 2000. We aggregated the GFC data from 1 arcsec to our 30 arcsec grid cells by summing the area of forest loss pixels weighted by their surface intersection with the 30 arcsec cells.ecoregions2017_code.tif: an integer with the ecoregions code (ECO_ID) rasterized from the Ecoregion 2017 polygons [Dinerstein et al., 2017; Resolve, 2017], which is available from [https://ecoregions2017.appspot.com/] under CC BY 4.0 license. The polygons were rasterized to a 30 arcsec grid by the major class present. The ecoregion class names corresponding to the GeoTIFF file values are available in the auxiliary file ecoregions_2017_concordance_tbl.csv, which contains the following variables ECO_ID, ECO_NAME, BIOME_NUM, BIOME_NAME, where ECO_ID is a unique identifier. The layers available from this repo can be stacked together with other variables essential for land-use modelling. Some of these variables are openly available at the same spatial extent and resolution, for example, grided population [NASA, 2018], elevation and slope [Amatulli et al., 2018a,b]

vwmaus/dtwSat: CRAN Release

Major release: Dependency Updates: This release removes obsolete dependencies, notably raster, rgdal, and sp. Reduced Dependencies: We have significantly minimized the number of package dependencies to streamline the installation and update process. Spatial Data Handling with sf and stars: Spatial data handling has been overhauled. We've introduced support for the sf and stars packages to enhance this capability. Improved Workflow Compatibility: The package now offers a workflow that aligns more seamlessly with other prevalent image classification workflows

Eroding resilience of deforestation interventions—evidence from Brazil’s lost decade

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