Measuring the consumer benefits of improving farm animal welfare to inform welfare labelling

Policy makers in the European Union are envisioning the introduction of a community farm animal welfare label which would allow consumers to align their consumption habits with their farm animal welfare preferences. For welfare labelling to be viable the market for livestock products produced to higher welfare standards has to be sufficiently segmented with consumers having sufficiently distinct and behaviourally consistent preferences. The present study investigates consumers’ preferences for meat produced to different welfare standards using a hypothetical welfare score. Data is obtained from a contingent valuation study carried out in Britain. The ordered probit model was estimated using Bayesian inference to obtain mean willingness to pay. We find decreasing marginal WTP as animal welfare levels increase and that people’s preferences for different levels of farm animal welfare are sufficiently differentiated making the introduction of a labelling scheme in the form of a certified rating system appear feasible

Simulating the impact on health of internalising the cost of carbon in food prices combined with a tax on sugar-sweetened beverages.

Rising greenhouse gas emissions (GHGEs) have implications for health and up to 30 % of emissions globally are thought to arise from agriculture. Synergies exist between diets low in GHGEs and health however some foods have the opposite relationship, such as sugar production being a relatively low source of GHGEs. In order to address this and to further characterise a healthy sustainable diet, we model the effect on UK non-communicable disease mortality and GHGEs of internalising the social cost of carbon into the price of food alongside a 20 % tax on sugar sweetened beverages (SSBs).Developing previously published work, we simulate four tax scenarios: (A) a GHGEs tax of £2.86/tonne of CO2 equivalents (tCO2e)/100 g product on all products with emissions greater than the mean across all food groups (0.36 kgCO2e/100 g); (B) scenario A but with subsidies on foods with emissions lower than 0.36 kgCO2e/100 g such that the effect is revenue neutral; (C) scenario A but with a 20 % sales tax on SSBs; (D) scenario B but with a 20 % sales tax on SSBs. An almost ideal demand system is used to estimate price elasticities and a comparative risk assessment model is used to estimate changes to non-communicable disease mortality.We estimate that scenario A would lead to 300 deaths delayed or averted, 18,900 ktCO2e fewer GHGEs, and £3.0 billion tax revenue; scenario B, 90 deaths delayed or averted and 17,100 ktCO2e fewer GHGEs; scenario C, 1,200 deaths delayed or averted, 18,500 ktCO2e fewer GHGEs, and £3.4 billion revenue; and scenario D, 2,000 deaths delayed or averted and 16,500 ktCO2e fewer GHGEs. Deaths averted are mainly due to increased fibre and reduced fat consumption; a SSB tax reduces SSB and sugar consumption.Incorporating the social cost of carbon into the price of food has the potential to improve health, reduce GHGEs, and raise revenue. The simple addition of a tax on SSBs can mitigate negative health consequences arising from sugar being low in GHGEs. Further conflicts remain, including increased consumption of unhealthy foods such as cakes and nutrients such as salt

The distributional and nutritional impacts and mitigation potential of emission-based food taxes in the UK

Agriculture and food production are responsible for a substantial proportion of greenhouse gas emissions. An emission based food tax has been proposed as one option to reduce food related emissions. This study introduces a method to measure the impacts of emission based food taxes at a household level which involves the use of data augmentation to account for the fact that the data record purchases and not consumption. The method is applied to determine the distributional and nutritional impacts of an emission based food tax across socio-economic classes in the UK. We find that a tax of £2.841/tCO2e on all foods would reduce food related emissions by 6.3% and a tax on foods with above average levels of emissions would reduce emissions by 4.3%. The tax burden falls disproportionately on households in the lowest socio-economic class because they tend to spend a larger proportion of their food expenditure on emission intensive foods and because they buy cheaper products and therefore experience relatively larger price increases

MyWay - Grundlagen zur Erweiterung von Mobilitäts-Apps um personalisiertes Routing für Personen mit Diversitätsmerkmalen. Abschlussbericht

The project MyWay (funded by mFUND, BMDV) looked at ways to improve the mobility of individuals with diverse permanent or situational characteristics by making mobility apps more diversity-conscious. The project addressed knowledge gaps and identified barriers that could potentially reduce the attractiveness of public transportation services. Applying qualitative and quantitative methods, mobility related obstacles and personal characteristics were identified and the relations between characteristics and means of transportation and infrastructure quantified. As a result, we developed what we call "obstacle profiles" that reflect the probability of an obstacle to affect a person with certain characteristics. Together with experts, we evaluated the technical, practical, and ethical requirements for integrating these obstacle profiles into mobility apps. As the study has shown, the integration of obstacle profiles into existing apps is technically feasible, but challenges exist in terms of data availability and service availability

Assessing the impact on chronic disease of incorporating the societal cost of greenhouse gases into the price of food: an econometric and comparative risk assessment modelling study

Objectives To model the impact on chronic disease of a tax on UK food and drink that internalises the wider costs to society of greenhouse gas (GHG) emissions and to estimate the potential revenue. Design An econometric and comparative risk assessment modelling study. Setting The UK. Participants The UK adult population. Interventions Two tax scenarios are modelled: (A) a tax of £2.72/tonne carbon dioxide equivalents (tCO2e)/100 g product applied to all food and drink groups with above average GHG emissions. (B) As with scenario (A) but food groups with emissions below average are subsidised to create a tax neutral scenario. Outcome measures Primary outcomes are change in UK population mortality from chronic diseases following the implementation of each taxation strategy, the change in the UK GHG emissions and the predicted revenue. Secondary outcomes are the changes to the micronutrient composition of the UK diet. Results Scenario (A) results in 7770 (95% credible intervals 7150 to 8390) deaths averted and a reduction in GHG emissions of 18 683 (14 665to 22 889) ktCO2e/year. Estimated annual revenue is £2.02 (£1.98 to £2.06) billion. Scenario (B) results in 2685 (1966 to 3402) extra deaths and a reduction in GHG emissions of 15 228 (11 245to 19 492) ktCO2e/year. Conclusions Incorporating the societal cost of GHG into the price of foods could save 7770 lives in the UK each year, reduce food-related GHG emissions and generate substantial tax revenue. The revenue neutral scenario (B) demonstrates that sustainability and health goals are not always aligned. Future work should focus on investigating the health impact by population subgroup and on designing fiscal strategies to promote both sustainable and healthy diets

The eNutri app: using diet quality indices to deliver automated personalised nutrition advice

Personalising nutrition advice using digital technologies, such as web-apps, offers great potential to improve users’ adherence to healthy eating guidelines. However, commercial offerings currently lack decision engines capable of delivering personalised nutrition advice. This article outlines the core concepts, content and features of the novel eNutri app, developed by researchers at the University of Reading. Uniquely, the app identifies and recommends food-based modifications that would be most beneficial for an individual taking into account both their current diet quality and their individual preferences

Overall and income specific effect on prevalence of overweight and obesity of 20% sugar sweetened drink tax in UK: econometric and comparative risk assessment modelling study

Objective To model the overall and income specific effect of a 20% tax on sugar sweetened drinks on the prevalence of overweight and obesity in the UK. Design Econometric and comparative risk assessment modelling study. Setting United Kingdom. Population Adults aged 16 and over. Intervention A 20% tax on sugar sweetened drinks. Main outcome measures The primary outcomes were the overall and income specific changes in the number and percentage of overweight (body mass index ≥25) and obese (≥30) adults in the UK following the implementation of the tax. Secondary outcomes were the effect by age group (16-29, 30-49, and ≥50 years) and by UK constituent country. The revenue generated from the tax and the income specific changes in weekly expenditure on drinks were also estimated. Results A 20% tax on sugar sweetened drinks was estimated to reduce the number of obese adults in the UK by 1.3% (95% credible interval 0.8% to 1.7%) or 180 000 (110 000 to 247 000) people and the number who are overweight by 0.9% (0.6% to 1.1%) or 285 000 (201 000 to 364 000) people. The predicted reductions in prevalence of obesity for income thirds 1 (lowest income), 2, and 3 (highest income) were 1.3% (0.3% to 2.0%), 0.9% (0.1% to 1.6%), and 2.1% (1.3% to 2.9%). The effect on obesity declined with age. Predicted annual revenue was £276m (£272m to £279m), with estimated increases in total expenditure on drinks for income thirds 1, 2, and 3 of 2.1% (1.4% to 3.0%), 1.7% (1.2% to 2.2%), and 0.8% (0.4% to 1.2%). Conclusions A 20% tax on sugar sweetened drinks would lead to a reduction in the prevalence of obesity in the UK of 1.3% (around 180 000 people). The greatest effects may occur in young people, with no significant differences between income groups. Both effects warrant further exploration. Taxation of sugar sweetened drinks is a promising population measure to target population obesity, particularly among younger adults

A health impact assessment of the UK soft drinks industry levy: a comparative risk assessment modelling study

Background In March, 2016, the UK government proposed a tiered levy on sugar-sweetened beverages (SSBs; high, moderate, and no tax for drinks with >8g, 5g to 8g, and <5g sugar per 100ml). We estimate the effect of possible industry responses to the levy on obesity, diabetes, and dental caries. Methods We modelled three possible industry responses: (1) reformulation to reduce sugar concentration, (2) increasing product price, and (3) changing the market share of high-, mid-, and low-sugar drinks. For each response, we defined a better and worse case health scenario. We developed a comparative risk assessment model to estimate the UK health impact of each scenario. Findings The best modelled scenario for health is SSB reformulation, resulting in 144,000 (95% uncertainty interval: 5,100 to 306,700) fewer adults and children with obesity in the UK, 19,000 (6,900 to 32,700) fewer incident cases of diabetes per year, and 269,000 (82,200 to 470,900) fewer decayed, missing, or filled teeth annually. Increasing the price of SSBs and changes to market share to increase the proportion of low-sugar drinks sold would also result in population health benefits, but to a lesser extent. The greatest benefit for obesity and oral health would be among individuals under 18 years, with people over 65 years experiencing the largest absolute decreases in diabetes incidence. Interpretation The health impact of the soft drink levy is dependent on its implementation by industry. There is uncertainty as to how industry will react and in the estimation of health outcomes. Health gains could be maximised by significant product reformulation with additional benefits possible if the levy is passed onto purchasers through raising the price of high- and mid-sugar drinks, and through activities to increase the market share of low-sugar products.RT and AK have previously done work on sugar-sweetened beverage taxes funded by the Union of European Soft Drinks Associations. MR is chair of Sustain and the Children's Food Campaign, which have campaigned for sugar drink taxes in the UK. MR is funded by the British Heart Foundation, grant number 006/PSS/CORE/2016/OXFORD. ADMB and OTM are members of the Faculty of Public Health, which has a position statement supporting sugary drink taxes. ADMB is funded by the Wellcome Trust, grant number 102730/Z/13/Z. OTM is a member of the UK Health Forum, which has also supported a UK sugar drinks tax. OTM is supported by a Wellcome Trust Clinical Doctoral Fellowship. SAJ was the independent Chair of the Department of Health Public Health Responsibility Deal Food Network from 2010 to 2015. SAJ is funded by the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care Oxford. The views expressed are those of the authors and not necessarily those of the National Health Service, National Institute for Health Research, or the Department of Health. PS is funded by the British Heart Foundation, grant number FS/15/34/31656. TB is funded the Health Research Council of New Zealand (16/443). AE declares no competing interests

Detecting and Estimating On-street Parking Areas from Aerial Images

Parking is an essential part of transportation systems and urban planning, but the availability of data on parking is limited and therefore posing problems, for example, estimating search times for parking spaces in travel demand models. This paper presents an on-street parking area prediction model developed using remote sensing and open geospatial data of the German city of Brunswick. Neural networks are used to segment the aerial images in parking and street areas. To enhance the robustness of this detection, multiple predictions over same regions are fused. We enrich this information with publicly available data and formulate a Bayesian inference model to predict the parking area per street meter. The model is estimated and validated using detected parking areas from the aerial images. We find that the prediction accuracy of the parking area model at mid to high levels of parking area per street meter is good, but at lower levels uncertainty increases. Using a Bayesian inference model allows the uncertainty of the prediction to be passed on to subsequent applications to track error propagation. Since only open source data serve as input for the prediction model, a transfer to structurally similar regions, for which no aerial images are available, is possible. The model can be used in a wide range of applications like travel demand models, parking regulation and urban planning

Parking space inventory from above: Detection on aerial images and estimation for unobserved regions

Parking is a vital component of today's transportation system and descriptive data are therefore of great importance for urban planning and traffic management. However, data quality is often low: managed parking places may only be partially inventoried, or parking at the curbside and on private ground may be missing. This paper presents a processing chain in which remote sensing data and statistical methods are combined to provide parking area estimates. First, parking spaces and other traffic areas are detected from aerial imagery using a convolutional neural network. Individual image segmentations are fused to increase completeness. Next, a Gamma hurdle model is estimated using the detected parking areas and OpenStreetMap and land use data to predict the parking area adjacent to streets. We find a systematic relationship between the road length and type and the parking area obtained. We suggest that our results are informative to those needing information on parking in structurally similar regions