A continuous increase in the world population and reduction in quantity and quality of resources is putting enormous pressure on nations to ensure a balance between food demand and supply. The same is highlighted at number two in the United Nations’ sustainable development goals as the Zero Hunger goal. However, a significant portion of produced food worldwide is damaged or wasted in the food supply chain. This study focuses on minimizing waste in the food supply chain by highlighting the importance of strategy, information systems, and network design in the food supply chain. The authors collected data from 211 respondents from 48 firms and analyzed it through structural equation modeling. It has been found that information system has great potential to minimize waste in the food supply chain. Moreover, strategy and network design significantly improve organizational capabilities to minimize waste in the food supply chain. The finding provides constructive insights to food companies to incorporate and operationalize recommended measures to minimize waste in the food supply chain so that the world can counter the food shortage and waste/damage problem and the Zero Hunger goal can successfully be achieved.

This paper introduces energy consumption and carbon emission into the analysis framework of the green productivity of tourism. By comparing and analyzing the two main methods used to evaluate the energy consumption and carbon emission estimations of tourism, namely, the “top-down” and “bottom-up” method, and considering the availability of data, the “bottom-up” method was adopted to evaluate the energy consumption and carbon emissions of tourism in the Yangtze River Economic Zone (YREZ). Then, using the Malmquist-Luenberger (ML) index in the super-efficiency data envelopment analysis (DEA) model, the green productivity of the tourism in 11 provinces and cities in the YREZ from 2006 to 2015 was measured. The empirical results show that: (1) The energy consumption and carbon emissions of tourism in the YREZ have increased steadily over the past 10 years, which has caused a certain degree of pollution to the environment, indicating that tourism is no longer a “smoke-free industry”; (2) there are significant provincial differences between the energy consumption and carbon emissions of tourism in the YREZ, with Shanghai always ranking first, while Guizhou and Yunnan ranks last, which represents that the tourism economic development level is positively correlated with the tourism energy consumption and carbon emissions; (3) the green productivity of tourism in the YREZ shows a fluctuating increasing trend in the past 10 years, and technological progress has become the main reason for its growth in green productivity; and (4) the green productivity of tourism in 11 provinces and cities in the YREZ can be divided into three types: Progressive type of tourism green development, stagnant type of tourism green development, and declining type of tourism green development. Consequently, different types of provinces should explore effective dependency paths based on their own conditions.

In the paper, the author establishes the "Hub-and-Spoke" logistics network of Wuhan city circle which includes core nodes that are Wuhan and Huangshi; the secondary nodes which includes Ezhou, Xiaogan and Huanggang and the third nodes that are Qianjiang, Xianning, Xiantao and Tianmen based on the "Hub-and-Spoke" logistics theory and the factor analysis. In the end of the paper, some suggestions about the development of the Hub-and-Spoke" logistics network in Wuhan city circle were proposed.

Multi-level hub-and-spoke logistics network is a special form of regional logistics and has been developed to a certain degree. Its construction should match with the development of regional economies. With the purpose of deducing cost and improving the efficiency of regional logistics, the paper has constructed and optimized a logistics network based on the data of regional economies. By using a set of cities as hub nodes with multi-levels according to the freight volume the calculated results were compared to the traditional network of single-level hub nodes which proves that the model with multi-level hub nodes is more economical and efficient.