Search results

This article aims to evaluate published food cold chain (FCC) literature against risk management and supply chain sustainability concepts.

The article uses the theory refinement logic proposed by Seuring et al. (2021) to analyze the contents of FCC management-related literature published over the past 20 years. A sample of 116 articles was gathered using Web of Science and subsequently analyzed. The respective articles were then systematically coded against the frameworks of Beske and Seuring (2014) and Vlajic et al. (2012), which focused on building sustainable and robust supply chains, respectively.

The literature review revealed that debates around managing contemporary sources of disruptions/vulnerability and making FCCs more sustainable and resilient are gradually developing. However, an overarching risk management perspective along with incorporating social and environmental dimensions in managing FCCs still needs the adequate attention of the respective research community.

The deductive internal logic of theory refinement approach used in this paper could have been further strengthened by using additional frameworks. This limitation, however, opens avenues for further research. The findings of the paper will stimulate the interest of future researchers to work on expanding our understanding related to sustainability and risk management in FCCs.

The paper is the first attempt to organize published FCC literature along dimensions of supply chain sustainability and risk management. The paper thus provides the respective researchers with a foundation that will help them adopt a focused approach to addressing the research gaps.

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO₂ emissions, farming areas and the labor force in China.

This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.

The Johansen test findings have shown that the fruit crop growth, energy use, CO₂ emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO₂ emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO₂ will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO₂ emissions to agricultural areas to fruit crops.

This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.