The environmental externalities of tobacco manufacturing: A review of tobacco industry reporting.

Growing research and public awareness of the environmental impacts of tobacco present an opportunity for environmental science and public health to work together. Various United Nations agencies share interests in mitigating the environmental costs of tobacco. Since 2000, transnational tobacco industry consolidation has accelerated, spotlighting the specific companies responsible for the environmental and human harms along the tobacco production chain. Simultaneously, corporate social responsibility norms have led the industry to disclose statistics on the environmental harms their business causes. Yet, independent and consistent reporting remain hurdles to accurately assessing tobacco's environmental impact. This article is the first to analyze publicly available industry data on tobacco manufacturing pollution. Tobacco's significant environmental impact suggests this industry should be included in environmental analyses as a driver of environmental degradation influencing climate change. Countries aiming to meet UN Sustainable Development Goals must act to reduce environmental harms caused by the tobacco industry

Complete Photoionization Experiments via Ultrafast Coherent Control with Polarization Multiplexing II: Numerics & Analysis Methodologies

The feasibility of complete photoionization experiments, in which the full set of photoionization matrix elements are determined, using multiphoton ionization schemes with polarization-shaped pulses has recently been demonstrated [Hockett et. al., Phys. Rev. Lett. 112, 223001 (2014)]. Here we extend on our previous work to discuss further details of the numerics and analysis methodology utilised, and compare the results directly to new tomographic photoelectron measurements, which provide a more sensitive test of the validity of the results. In so doing we discuss in detail the physics of the photoionziation process, and suggest various avenues and prospects for this coherent multiplexing methodology

Spatial aspects of the design and targeting of agricultural development strategies:

Two increasingly shared perspectives within the international development community are that (a) geography matters, and (b) many government interventions would be more successful if they were better targeted. This paper unites these two notions by exploring the opportunities for, and benefits of, bringing an explicitly spatial dimension to the tasks of formulating and evaluating agricultural development strategies. We first review the lingua franca of land fragility and find it lacking in its capacity to describe the dynamic interface between the biophysical and socioeconomic factors that help shape rural development options. Subsequently, we propose a two-phased approach. First, development strategy options are characterized to identify the desirable ranges of conditions that would most favor successful strategy implementation. Second, those conditions exhibiting important spatial dependency – such as agricultural potential, population density, and access to infrastructure and markets – are matched against a similarly characterized, spatially-referenced (GIS) database. This process generates both spatial (map) and tabular representations of strategy-specific development domains. An important benefit of a spatial (GIS) framework is that it provides a powerful means of organizing and integrating a very diverse range of disciplinary and data inputs. At a more conceptual level we propose that it is the characterization of location, not the narrowly-focused characterization of land, that is more properly the focus of attention from a development perspective. The paper includes appropriate examples of spatial analysis using data from East Africa and Burkina Faso, and concludes with an appendix describing and interpreting regional climate and soil data for Sub-Saharan Africa that was directly relevant to our original goal.Spatial analysis (Statistics), Agricultural development., Burkina Faso., Africa, Sub-Saharan.,

Forecasting economic activity with higher frequency targeted predictors

In this paper we explore the performance of bridge and factor models in forecasting quarterly aggregates in the very short-term subject to a pre-selection of monthly indicators. Starting from a large information set, we select a subset of targeted predictors using data reduction techniques as in Bai and Ng (2008). We then compare a Diffusion Index forecasting model as in Stock and Watson (2002), with a Bridge model specified with an automated General-To-Specific routine. We apply these techniques to forecasting Italian GDP growth and its main components from the demand side and find that Bridge models outperform naive forecasts and compare favorably against factor models. Results for France, Germany, Spain and the euro area confirm these findings.short-term GDP forecast, factor models, bridge models, General To Specific

What drives euro area break-even inflation rates?

The yield spread between nominal and inflation-linked bonds (or break-even inflation rates, BEIR) is a fundamental indicator of inflation expectations (and associated premia). This paper investigates which macroeconomic and financial variables explain BEIRs. We evaluate a large number of potential explanatory variables through Bayesian model selection techniques and document their explanatory power at different horizons. At short horizons, actual inflation dynamics is the main determinant of BEIRs. At long horizons, financial variables (i.e. term spread, bond market volatility) become increasingly relevant, but confidence and cyclical indicators remain important. JEL Classification: C11, C52, E31Bayesian model selection, break-even inflation rates, business cycle indicators, inflation risk premia