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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.

In recent years, developing countries, especially resource-dependent regions, have been facing the paradox of ensuring both emissions reduction and economic development. Thus, there is a strong political desire to forecast carbon emissions reduction potential and the best way to achieve it. This study constructs a methodology to assess carbon reduction potential in a resource-dependent region. The Simulated Annealing Programming algorithm and the Genetic algorithm were introduced to create a prediction model and an optimized regional carbon intensity model, respectively. Shanxi Province in China, a typical resource-dependent area, is selected for the empirical study. Regional statistical data are collected from 1990 to 2015. The results show that the carbon intensity of Shanxi Province could drop 18.78% by 2020. This potential exceeds the 18% expectation of the Chinese Government in its '13th Five-Year Work Plan' for Controlling Greenhouse Gas Emissions. Moreover, the carbon intensity of the province could be further reduced by 0.97 t per 10,000 yuan GDP. The study suggests that the carbon emissions of a resource-dependent region can be reduced in the following ways; promoting economic restructuring, upgrading coal supply-side reform, perfecting the self-regulation of coal prices, accelerating the technical innovation of the coal industry, and establishing a flexible mechanism for reducing emissions.