Climatic burden of eating at home against away-from-home: A novel Bayesian Belief Network model for the mechanism of eating-out in urban China

Carbon Footprint of Away-From-Home Food Consumption in Brazilian Diet

Environmentally unsustainable diets are often characterized by being high in calories, processed foods, and red meats, characteristics related to away-from-home food (AFHF). The aim of this study is to evaluate if AFHF consumption is related to environmental sustainability. Data of 20,780 adults from 24 h recalls collected in the 2017-2018 Brazilian National Dietary Survey (NDS) were used to estimate carbon footprint coefficients. The mean carbon footprint was estimated among individuals who consumed AFHF and non-consumers. Linear regression models were used to evaluate differences between away-from-home eating and the carbon footprint of the diet, adjusting for age and income. A total of 41% of Brazilians consumed AFHF during the previous day. The mean carbon footprint from foods consumed away from home represented 18% of the total carbon footprint. AFHF was positively associated with increased total carbon footprint (β: 204.1; p-value: 0.0145). In conclusion, the consumption of foods away from home in urban areas of Brazil was associated with atmospheric greenhouse gas emissions independently of age and income. Away-from-home food consumption should be considered to reinforce the influence of diet on individual and planet health.

Life cycle environmental impacts of food away from home and mitigation strategies-a review

Food production and consumption are major drivers of global environmental change, endangering the safe operating space of many environmental areas. Globally, there has been a growing trend of dining out, termed food away from home (FAFH) here, but its environmental sustainability has received insufficient attention. In this review, we examine studies quantifying the life-cycle environmental impacts of FAFH and identify mitigation strategies across the food supply chain. Overall, previous life cycle assessment (LCA) studies focused on the composition of FAFH meals and pre-use life cycle stages, especially food production. Greenhouse gas (GHG) emissions of FAFH meals range from 0.134 kg CO₂ e/meal to 13.2 kg CO₂ e/meal for school canteen meals, and from 0.60 kg CO₂ e/meal to 9.6 kg CO₂ e/meal for other catering services. Meat ingredients are the dominant source in a variety of environmental impact categories, and the food production stage usually accounts for over half of the total GHG emissions in the FAFH life cycle. Supply side mitigation strategies include advancing farming practices, updating cold transportation technology, and improving building energy efficiency. Demand side mitigation focuses on dietary change towards meals with less meat ingredients, with nudging and sustainable menu-designing as the two primary groups of strategies. Areas of focus for LCA include improving modeling of building energy consumption related to food consumption, advancing uncertainty characterization of life cycle results, and capturing geographical variations in food production.

Data-driven Bayesian network modelling to explore the relationships between SDG 6 and the 2030 Agenda

The Sustainable Development Goals (SDGs) are presented as integrated and indivisible. Therefore, for monitoring purposes, conventional indicator-based frameworks need to be combined with approaches that capture and describe the links and interdependencies between the Goals and their targets. In this study, we propose a data-driven Bayesian network (BN) approach to identify and interpret SDGs interlinkages. We focus our analysis on the interlinkages of SDG 6, related to water and sanitation, across the whole 2030 Agenda, using SDG global available data corresponding to 179 countries, 16 goals, 28 targets and 44 indicators. To analyze and validate the BN results, we first demonstrate the robustness of the BN approach in identifying indicator relationships (i.e. consistent results throughout different country sample sizes). Second, we show the coherency of the results by comparing them with an exhaustive study developed by UN-Water. As an added value, our data-driven approach provides further interlinkages, which are contrasted against the existing literature. We conclude that the approach adopted is useful to accommodate a thorough analysis and interpretation of the complexities and interdependencies of the SDGs.

Spatiotemporal variability in urban HORECA food consumption and its ecological footprint in China

Increasing hotel/restaurant/café (HORECA) food consumption (HFC) has become one of the most prominent features of food consumption transformation under the background of rapid urbanization in China. Combining direct-weighing data (using 11,883 dishes in 164 restaurants) and literature data, this study, for the first time, quantitatively calculated the amount of HFC and its ecological footprint (EF) in China in 2002 and 2015 to depict the environmental effects of this transformation. The results indicated that per capita HFC tripled from 320 g/cap/meal in 2002 to 852 g/cap/meal in 2015, representing an average annual growth rate of 13%. In addition, by scaling up the HFC in 31 provinces/municipalities/autonomous regions to the national scale, HFC in China increased by 12,612 tons from 2002 to 2015. Finally, the total EF in the HORECA sector increased nearly 33 times (or 44,440 global ha), from 1348 global ha in 2002 to 45,788 global ha in 2015. The EF of Guangdong increased the most, surpassing 4000 global ha in 2015. These findings provide a scientific basis to support improved food resource spatial allocation and mitigation of regional resource pressure to achieve sustainable consumption under the current background of rapid urbanization in China.

Bayesian network modelling of hierarchical composite indicators

The water, sanitation and hygiene (WaSH) sector has witnessed the development of multiple tools for multidimensional monitoring. Hierarchical and composite indicator (CI)-based conceptual frameworks provide one illustrative example. However, this approach does not address the existing interrelationship of the indicators they comprise. Bayesian networks (BNs) are increasingly being exploited to assess WaSH issues and to support planning and decision-making processes. Here, we aim to evaluate the validity, reliability and feasibility of BNs in replicating an existing CI-based conceptual framework. We adopt a data-driven approach and propose a semi-automatic methodology. As a pilot study, we used the regional monitoring initiative Rural Water Supply and Sanitation Information System (SIASAR). Data from two different countries are processed and analysed to calibrate and validate the model and the method. The major findings show: i) the model inference capacity improves when structure is provided to the networks (according to the CI-based framework); ii) key components that explain a pre-defined objective variable are reduced and quantified (implying important advantages in data updating); and iii) interlinkages among these components can be identified (which might enhance multi- and trans-disciplinary actions). We conclude that BNs accurately replicate the CI-based conceptual framework, with great potential for a wider application.

The potential water-food-health nexus in urban China: A comparative study on dietary changes at home and away from home

In China, urbanization strengthens the water-food-health nexus by driving dietary changes both at home and away from home (AFH). However, few studies have compared the effects of dining location on water footprint generation and/or linked such habits to the prevalence of non-communicable diseases (NCDs), such as hypertension, diabetes, asthma, myocardial infarction, stroke and cancer. Here, household survey data were used to develop a multiple-component urbanization sequence, on which the diet-related water footprint was quantified and the mechanisms under the water-food-health nexus were explored. Significant dietary changes due to urbanization have occurred at home (instead of AHF), and increases or decreases in various food groups are stratified across dining locations. Log mean Divisia index decomposition shows that the diet-structure effect outweighing the intake effect dominates the water footprint changes during China's urbanization. Animal products contribute 92(94)% of the diet-structure effect on net water footprint growth at home(AFH); in contrast, vegetal foods dominate the intake effect, contributing 67(49)%. The at-home water footprint ratio of animal products to vegetal foods is highly related to the prevalence of diabetes, hypertension, cancer, and asthma, and reaching statistical significance. Two urbanization components, namely housing situation and community diversity, serve as the key drivers of water-food-health nexus enhancement in urbanized China. Due to the complexity of water-food-health links, nexus thinking is needed to benefit human health and diet-related water consumption; besides, it may be reasonable to expand current dimension of food-energy-water nexus topic to include health issues.