The coordinated development of industrialization and its ecological environment are vital antecedents to sustainable development in China. However, along with the accelerating development of industrialization in China, the contradiction between industrial development and environment preservation has turned out to be increasingly evident and inevitable. Eco-efficiency can be seen either as an indicator of environmental performance, or as a business strategy for sustainable development. Hence, industrial eco-efficiency promotion is the key factor for green industrial development. This study selects indicators relevant to resources, economy, and the environment of industrial development, and the indicators can well reflect the characteristics of industrial eco-efficiency. The SBM (Slacks-Based Measure) model overcomes the limitations of a radial model and directly accounts for input and output slacks in the efficiency measurements, with the advantage of capturing the entire aspect of inefficiency. This study evaluates the industrial eco-efficiency of nine cities in Fujian province during the period of 2006–2016, based on undesired output SBM (Slacks-Based Measure) model and also uses a Tobit regression model to analyze the influencing factors. The results show that there is a positive correlation among the economic development level, opening level, research and development (R&D) innovation, and industrial eco-efficiency in Fujian Province. However, a negative correlation was found between the industrial structure and industrial eco-efficiency in Fujian Province. Moreover, environmental regulation in Fujian Province was not found to significantly influence the industrial eco-efficiency. Hence, through the systematic analysis of industrial eco-efficiency and its influencing factors in Fujian, the study gives further insight on how policy-making can help achieve sustainable development, balancing between economic benefits and ecological improvements.

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With the popularity of the Internet and mobile terminals, the development of e-commerce has become hotter. Therefore, e-commerce research starts to focus on the statistics and prediction of the cargo volume of logistics. This study briefly introduced the back-propagation (BP) neural network model and principal component analysis (PCA) method and combined them to obtain an improved PCA-BP neural network model. Then the traditional BP neural network model and the improved PCA-BP neural network model were used to perform the empirical analysis of the cold chain logistics demand of fruits and vegetables in city A from 2010 to 2018. The results showed that the main factors that affected the local cold chain logistics demand were the growth rate of GDP, the added value of primary industry, the planting area of fruits and vegetables, and the consumption price index of fruits and vegetables; both kinds of neural networks model could effectively predict the cold chain logistics demand, but the predicted value of the PCA-BP neural network model was more fitted with the actual value. The prediction error of the BP neural network model was larger, and the fluctuation was obvious within the prediction interval. Moreover, the time required for the prediction by the PCA-BP neural network model was less than that by the BP neural network model. In summary, the improved PCA-BP neural network model is faster and more accurate than the traditional BP model in predicting the cold chain logistics demand.

Purpose The purpose of this study was to measure the innovative performance of a managed and owned mainland Chinese family business. The objective of the study was to assist an inheritor and/or successor of a family business and to find management problems in innovative activity. Design/methodology/approach To improve the innovative technical efficiency (TE) of the business, the study offers methods that enhance the allocation of resources to provide outcomes that improve the core competitiveness of the business and realize the sustainable development of the business. Innovation performance is a well-organized and efficient way of turning innovation input into innovation output. Human input, research and development expenditures measure innovation input. Patent output and other outputs, which include total labor productivity and asset liability ratios, measure innovation output. To complete the study’s task, the innovative performance of 46 Chinese listed family run and owned businesses were evaluated based on the data envelopment analysis and the Banker, Charnes and Cooper model. Findings The results of the study show that the overall TE of innovation in a Chinese family run and owned business is low and that the returns to scale of most such businesses is decreasing, and furthermore, that the overall innovation performance of is low. Originality/value The implications from the study further suggest that for business efficiency and increased profit a beneficiary of a Chinese family-owned business should optimize the firm’s size and resource allocation.