Guobao Song

Food consumption and waste and the embedded carbon, water and ecological footprints of households in China

Strategies for reducing food waste and developing sustainable diets require information about the impacts of consumption behavior and waste generation on climatic, water, and land resources. We quantified the carbon, water, and ecological footprints of 17,110 family members of Chinese households, covering 1935 types of foods, by combining survey data with available life-cycle assessment data sets. We also summarized the patterns of both food consumption and waste generation and analyzed the factors influencing the observed trends. The average person wasted (consumed) 16 (415) kg of food at home annually, equivalent to 40 (1080) kg CO2e, 18 (673) m(3), and 173 (4956) gm(2) for the carbon, water and ecological footprints, respectively. The generation of food waste was highly correlated with consumption for various food groups. For example, vegetables, rice, and wheat were consumed the most and accounted for the most waste. In addition to the three plant-derived food groups, pork and aquatic products also contributed greatly to embedded footprints. The data obtained in this study could be used for assessing national food security or the carrying capacity of resources.

Dietary changes to mitigate climate change and benefit public health in China

Dietary change presents an opportunity to meet the dual challenges of non-communicable diseases and the effects of climate change in China. Based on a food survey and reviewed data sets, we linked nutrient composition and carbon footprint data by aggregating 1950 types of foods into 28 groups. Nine dietary scenarios for both men and women were modeled based on the current diet and latest National Program for Food and Nutrition. Linear uncertainty optimization was used to produce diets meeting the Chinese Dietary Reference Intakes for adults aged 18-50years while minimizing carbon footprints. The theoretical optimal diet reduced daily footprints by 46%, but this diet was unrealistic due to limited food diversity. Constrained by acceptability, the optimal diet reduced the daily carbon footprints by 7-28%, from 3495 to 2517-3252g CO2e, for men and by 5-26%, from 3075 to 2280-2917g CO2e, for women. Dietary changes for adults are capable of benefiting China in terms of the considerable footprint reduction of 53-222Mt.CO2eyear-1, when magnified based on the Chinese population, which is the largest worldwide. Seven of eight scenarios showed that reductions in meat consumption resulted in greater reductions in greenhouse gas emissions. However, dramatic reductions in meat consumption may produce smaller reductions in emissions, as the consumption of other ingredients increases to compensate for the nutrients in meat. A trade-off between poultry and other meats (beef, pork, and lamb) is usually observed, and rice, which is a popular food in China, was the largest contributor to carbon footprint reductions. Our findings suggest that changing diets for climate change mitigation and human health is possible in China, though the per capital mitigation potential is slight lower than that in developed economies of France, Spain, Sweden, and New Zealand.

Risk Assessment and Hierarchical Risk Management of Enterprises in Chemical Industrial Parks Based on Catastrophe Theory

According to risk systems theory and the characteristics of the chemical industry, an index system was established for risk assessment of enterprises in chemical industrial parks (CIPs) based on the inherent risk of the source, effectiveness of the prevention and control mechanism, and vulnerability of the receptor. A comprehensive risk assessment method based on catastrophe theory was then proposed and used to analyze the risk levels of ten major chemical enterprises in the Songmu Island CIP, China. According to the principle of equal distribution function, the chemical enterprise risk level was divided into the following five levels: 1.0 (very safe), 0.8 (safe), 0.6 (generally recognized as safe, GRAS), 0.4 (unsafe), 0.2 (very unsafe). The results revealed five enterprises (50%) with an unsafe risk level, and another five enterprises (50%) at the generally recognized as safe risk level. This method solves the multi-objective evaluation and decision-making problem. Additionally, this method involves simple calculations and provides an effective technique for risk assessment and hierarchical risk management of enterprises in CIPs.

A Bayesian belief network modelling of household factors influencing the risk of malaria

Studies that focus on integrated modelling of household factors and the risk for malaria parasitaemia among children in sub-Saharan Africa (SSA) are scarce. By using Malaria Indicator Survey, Demographic Health Survey, AIDS Indicator Survey datasets, expert knowledge and existing literature on malaria, a Bayesian belief network (BBN) model was developed to bridge this gap. Results of sensitivity analysis indicate that drinking water sources, household wealth, nature of toilet facilities, mothers educational attainment, types of main wall, and roofing materials, were significant factors causing the largest entropy reduction in malaria parasitaemia. Cattle rearing and residence type had less influence. Model accuracy was 86.39% with an area under the receiver-operating characteristic curve of 0.82. The models spherical payoff was 0.80 with the logarithmic and quadratic losses of 0.53 and 0.35 respectively indicating a strong predictive power. The study demonstrated how BBN modelling can be used in determining key interventions for malaria control. Display Omitted A Bayesian Belief Networks model is developed from household factors to predict malaria parasitaemia risk among children.Datasets of Malaria Indicator Survey, Demographic Health Survey and AIDS indicator survey are used to compile the model.Malaria parasitaemia risk increases in households using borehole water, dug wells and surface water points.A BBN model can be used in determining key interventions for malaria control.

Estimation of crop residue in China based on a Monte Carlo analysis

Accurate crop residue resource estimation is important for bioenergy development. This is done by the ratio of residue to grain (R/G), which is usually regarded constant and is widely used for crop residue estimation though uncertainty is inevitable in practice. In this study, a Monte Carlo algorithm was applied to estimate national crop residue by R/G taken from published reports in China. The estimated result was further mapped in pixels by geographic information system. In 2009, the amount of crop residue was found to be 802.32 million tons (Mt), with 679.36 and 947.28 Mt as the lower and upper limits for 95% confidence limits. Chinese crop residue was dominated by rice, wheat, and corn, accounting for 74.57% (598.29 Mt). From 1949 to 2009, the amount of crop residue increased by four times, accompanied by component change. The spatial distribution of crop residue in China is markedly heterogeneous. Compared to the shortage of crop residue in northwest China, there is an abundant crop residue of about 334 Mt in eastern China, attracting 90% of the country’s electricity or heat generation plants.

A dynamic model for population mapping: a methodology integrating a Monte Carlo simulation with vegetation-adjusted night-time light images

Population is attracting increasing attention as a driver of resource overexploitation, environmental degradation, loss of biodiversity, and other environmental challenges. Timely and accurately updating maps of population distribution are thus urgently needed. Images of night-time lights from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) have been used for years in population mapping as an alternative to human settlement distribution. The capacity of night-time light images for gridding populations, however, is compromised by the dual effects of saturation and overglow. Static models of the human settlement index (HSI), elevation-adjusted human settlement index (EAHSI), and vegetation-adjusted night-time light urban index (VANUI) have been developed to counteract these negative effects by using constant coefficients. The static models, however, retain disadvantages due to the negative effects of the high variation of socio-economic backgrounds in different study areas. In this study, we integrate Monte Carlo simulation with the above three static indices and propose the dynamic model VANUI Supported by Monte Carlo Simulation (VANUIMCS) for mapping the population of Liaoning Province, China. We assess the accuracy of the simulation using data for 60 counties and 1251 townships. The VANUIMCS improve the accuracy of population mapping, with the mean percentage errors of 19.43% at the county level and 43.19% at the township level.

Assessing Major Accident Risks to Support Land-Use Planning Using a Severity-Vulnerability Combination Method: A Case Study in Dagushan Peninsula, China.

Major accident risks posed by chemical hazards have raised major social concerns in todays China. Land-use planning has been adopted by many countries as one of the essential elements for accident prevention. This article aims at proposing a method to assess major accident risks to support land-use planning in the vicinity of chemical installations. This method is based on the definition of risk by the Accidental Risk Assessment Methodology for IndustrieS (ARAMIS) project and it is an expansion application of severity and vulnerability assessment tools. The severity and vulnerability indexes from the ARAMIS methodology are employed to assess both the severity and vulnerability levels, respectively. A risk matrix is devised to support risk ranking and compatibility checking. The method consists of four main steps and is presented in geographical information-system-based maps. As an illustration, the proposed method is applied in Dagushan Peninsula, China. The case study indicated that the method could not only aid risk regulations on existing land-use planning, but also support future land-use planning by offering alternatives or influencing the plans at the development stage, and thus further enhance the roles and influence of land-use planning in the accident prevention activities in China.

Shift from feeding to sustainably nourishing urban China: A crossing-disciplinary methodology for global environment-food-health nexus

Dietary change is a win-win opportunity to address the nexus of health and the environment. To prevent city dwellers from developing non-communicable diseases, in 2013, China updated the 2000 version of nutrition-based dietary reference intake (DRI) guidelines. However, whether the DRI guidelines have a positive effect on the environment is not well understood. Here, we explored the systematic effects of urbanization on Chinas health and environmental nexus based on survey data. Then, we optimized the diets of 18 age-gender groups to reduce carbon emissions, water consumption, and land use while meeting the healthy nutrition goals of both DRI guidelines. The results showed that the optimal diets based on the DRI 2013 outperformed these on DRI 2000 in improving Chinas environmental sustainability, although these diets did not always perform better at an individual scale. Our findings suggest that dietary changes can reduce carbon, water, and ecological footprints by 24%, 15%, and 22% in 2050, respectively; however, the differences in age-specific and gender-specific health goals cannot be neglected.

Quantitative modeling of freshwater stress in the nine water basins of Tanzania

Freshwater scarcity is a global issue of environmental concern that threatens agricultural production and human health. In this study, we established freshwater stress indices (WSIs) for the nine water basins of Tanzania by using the quantity of freshwater available and various water uses. The relationship between water availability and different water uses, including environmental water requirements, was analyzed, with uncertainty and sensitivity analysis performed by a Monte Carlo simulation technique. Extreme WSI values close to 1.00 were obtained in the Rufiji, Pangani, and Wami-ruvu basins, Internal drainage, and Lake Rukwa, while low and moderate WSI values ranging from 0.03 to 0.84 were found in Lake Victoria and the Ruvuma, Tanganyika, and Nyasa basins. This study adds further knowledge on the level of freshwater scarcity, relationships between water availability and different water uses, and suggests policy options to reduce freshwater scarcity at the basin level for sustainable water supply.

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

Dietary patterns of eating away-from-home (AFH) considerably differ from those of eating at home in urban China, thus generating varied carbon footprints. However, few studies have investigated the effect of eating places on diet-related climatic burden, and few have modelled the mechanism under the condition of eating-out because the decision of consumers on whether to eat AFH or at home is determined by multiple non-linear socioeconomic factors. Here, we compared the carbon footprints of eating at home and AFH using household survey data from 12 Chinese provinces, and developed a Bayesian Belief Network (BBN) model to identify key factors of eating AFH. Our findings show that eating AFH leads to higher climatic burdens though respondents consume less food on average than when eating at home. However, in urban areas, the carbon footprint generated increases more rapidly from eating at-home than when eating AFH. The BBN model was found to have strong capability to predict the possibility of eating out with an accuracy of 89%. Although diet patterns and embedded carbon footprint vary considerably across provinces from northeastern to southwestern China, sufficient evidence could not be found to support the influence of geographic factors on the decision of respondents to eat AFH at large scale. Instead, individual occupation and income were found to be the two key contributors. Thus, merely estimating the carbon footprint of food consumption is currently not sufficient, but social and economic elements need to be quantitatively considered to differentiate the eating-place effect on diet-related climatic burden.