Food waste has caused double waste of resources in the food itself and the food supply chain of production, transportation, cooking, and processing, resulting in unnecessary greenhouse gas emissions and economic losses. This paper first conducted the quantification of students’ food waste and the collection of canteens operation data in the three canteens at Taiyuan University of Technology (China) in 2019 through the weighing method and interview. Then an integration of Life Cycle Assessment and Life Cycle Costing was used to quantify the impact of food waste in university canteens on the environment and costs. The study found that the total amount of food waste in the university canteens with 22,000 students was 246.75 t/a, the carbon footprint caused by food waste was 539.28 t CO₂-eq, and the cost was 4,729,900 yuan. Most of the impact of canteen meals on the environment comes from the use of energy in food cooking and the consumption of animal food types. The innovative integration of life cycle cost calculations highlights the key role of the labor required for cooking. The research results answer the basic scientific questions of how much food is wasted in the university canteens, and the carbon emissions and cost ratios of these wasted food in all links of the supply chain. The research results can provide a policy-making basis and data support for reducing food waste in universities and realizing carbon emission reduction in university canteens.

To improve energy-use sustainability in cities, we proposed a set of urban energy performance assessment indicators and influencing factors based on existing theory and literature. An urban energy performance assessment and influencing factor model was also constructed by the improved stochastic frontier analysis method, and panel data from provincial capitals in China from 2004 to 2013 were considered as an example to carry out an empirical study. Chosen from both endogenous and exogenous perspectives, the urban energy performance assessment indicators and influencing factors take into consideration the capital, labor, energy, urban economic output, urbanization level, population, area, urban climate, and travel selection. Because it considers both random errors and the inefficiency levels of urban productions, the urban energy performance assessment and influencing factor model could reduce the errors caused by two-stage performance assessment and factor analysis, quantify the effects of assessment indicators and influencing factors on urban energy performance, and reflect the actual performance of different cities. Empirical results show that the urban energy performance of provincial capitals in China has been increasing. Chinese provincial capitals also have great potential for energy saving. It was necessary to include energy input as an assessment indicator when evaluating urban energy performance. Population density and urban energy performance showed a negative correlation, but the urbanization rate, temperature index, and household car ownership were positively related to urban energy performance. The urban energy performance of Chinese provincial capitals gradually decreased from east to west. Based on these results, several policy suggestions on urban energy performance development are proposed.

In order to clarify the evolution characteristics and direction of urban energy performance concepts, reveal the research dimensions, determine the performance results and differences, and clarify the reference benchmark, this study depicts the main systems involved in the process of urban energy utilization, demonstrates their relevance guided by the system view, and proposes the measurement indicators in the economic, environmental, and well-being dimensions. The measurement model of each dimension is constructed using the corresponding models of Data Envelopment Analysis. Taking 142 prefecture level cities in China as examples, the energy performance in different dimensions is measured and compared. The energy performance levels are close in the economic and environmental dimensions. However, the results of the well-being dimension are different from these first two dimensions, and the performance levels among cities differ more. In the economic, environmental, and well-being dimension, 22, 28 and 16 cities have reached the effective frontier, respectively, and the performance benchmark cities of 15, 15 and 5 provinces are non-provincial capital cities, respectively. Based on the above analysis, the “chain” framework evolution direction of concept and measurement is proposed, and this study provides benchmarks and policy suggestions to improve energy performance.