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Animal-source foods are a major component of global diets and are increasingly criticised because of their adverse impacts on environment, climate and health. A shift in diets towards plant-based foods is a discussed option to overcome these problems. Much of the scientific emphasis so far has been on estimating the potential of such a dietary change to reduce greenhouse gas emissions and improve health outcomes while less attention has been attracted on studies analysing the impacts on agricultural markets. This paper aims to provide a comprehensive overview and, therefore, summarizes existing studies on the effects of a reduced consumption of animal-source foods on agricultural markets, greenhouse gas emissions, food security and health. In addition, available studies on the so‑called rebound effect are presented. The identified studies suggest that a reduction in the consumption of meat in the EU or OECD would lead to a 1‑10% decrease in meat world market prices, depending on the magnitude and particularities of the assumed dietary changes. This would translate to a 3‑10% reduction in production. The lower domestic demand for meat could also negatively affect welfare outcomes and GDP. However, it has to be mentioned that these studies do not take into account the consequences of improved environmental and health conditions. In fact, our review indicates that reductions in greenhouse gas emissions could generally be proportional to the magnitude of plant-based diets. The maximum reduction potentials of 60-70% could be found for global vegetarian or vegan diets. However, some studies indicate that a shift in food expenditure towards other resource-intensive goods could lead to a rebound effect. Further, this overview suggests that environmental and public health objectives might be in alignment as all identified studies indicate that a reduction in meat consumption in high‑income countries could be associated with lower rates of mortality and non-communicable diseases. This overview reveals the complex relationships between food demand, agricultural supply, international trade, environment, health and food security.

The United Kingdom’s food system will be greatly impacted by Brexit-related trade deals and policy developments—with implications for dietary risk factors and public health. Here we use an integrated economic–health modelling framework to analyse the impacts of different policy approaches to Brexit. A ‘soft Brexit’ that is in line with the EU–UK Trade and Cooperation Agreement increases diet-related mortality in the United Kingdom as costs for health-promoting and import-dependent foods increase and their consumption decreases. Negotiating free-trade agreements with the United States and Commonwealth countries as part of a ‘global Britain’ approach could triple the negative health impacts of Brexit as greater availability of energy-dense foods increases weight-related risks without meaningfully reducing dietary risks. Eliminating import tariffs on health-sensitive horticultural products could mitigate the negative health impacts of Brexit, and reforming agricultural subsidies to incentivize greater domestic horticultural production could lead to net health gains. Combining these health-sensitive approaches to trade policy and subsidy reform doubled the health gains, offering a promising approach for mitigating the detrimental impacts Brexit could have on dietary health in the United Kingdom. Post-Brexit free-trade agreements could increase diet-related health risks—but tariff and subsidy reforms could mitigate these risks.

In this research paper, we present a system for named entity recognition and automatic document classification in an innovative knowledge management system for Applied Gaming. The objective of this project is to facilitate the management of machine learning-based named entity recognition models, that can be used for both: extracting different types of named entities and classifying text documents from different sources on the Web. We present real-world use case scenarios and derive features for training and managing NER models with the Stanford NLP machine learning API. Then, the integration of our developed NER system with an expert rule-based system is presented, which allows an automatic classification of text documents into different taxonomy categories available in the knowledge management system. Finally, we present the results of two evaluations. First, a functional evaluation that demonstrates the portability of our NER system using a standard text corpus in the medical area. Second, a qualitative evaluation that was conducted to optimize the overall user interface of our system and enable a suitable integration into the target environment.

We use the unexpected partial repeal of a tax break for commuters in Germany to examine the distribution of benefits from commuting subsidies between workers and firms. Drawing on a large set of geo-referenced employer-employee data, we use exact route distances between place of work and place of residence to calculate individual net wage benefits from commuting subsidies. In line with urban efficiency wage theories, we find robust evidence that employers compensate workers on average for about one third of the net wage loss caused by the reform if wages are individually negotiated. We find no comparable effect for workers covered by collective wage agreements. The subsequent existence of two common subsidy regimes within an otherwise stable institutional environment allows to draw inference on how each regime redistributes income between wage groups and between regions. We find that the introduction of a lower bound for commuting distances leads to a more equal distribution of net wage benefits between wage groups and regions compared to a regime without a lower bound.