Kai He

This study evaluates the eco-efficiency of China's industrial systems using a novel three-layer meta-frontier directional distance function based dynamic network data envelopment analysis model. It highlights the industrial sector's vital role in the national economy and its influence on ecological sustainability and societal welfare. The findings indicate that overall eco-efficiency at the national level is suboptimal, with significant disparities among provincial industrial systems regarding scale, cleaner production technologies, and management standards. The western region shows lower scale efficiency (SE) and management efficiency (ME) but higher technical efficiency (TE). In contrast, the central region performs well in both SE and ME, while the eastern region faces challenges with TE. The research reveals a consistent improvement in China's industrial system's average global meta-frontier Malmquist-Luenberger index, with a 21.15% increase in total factor eco-efficiency from 2011 to 2020, primarily driven by advancements in scale efficiency. Finally, the study recommends tailored strategies to enhance eco-efficiency by addressing specific inefficiencies within each provincial industrial system.

The main objective of this research is to measure the efficiency of 397 major industrial enterprises in Sichuan province of China in 2013.To this end, we employed DEA super slacks-based measure (Super-SBM) model for performance evaluation of 397 major manufacturing firms.The empirical results show that 21 of the 397 enterprises operate efficiently, and the average efficiency score of the analyzed enterprises is only 0.15. The enterprise with the highest efficiency score is 96.15% higher than the average score, which is the benchmark enterprise of operational efficiency. Among the selected sample enterprises, 5.29% of the industrial enterprises are highly efficient in operation. It was also noticed that the average efficiency score of pharmaceutical firms was the highest among all industrial firms with a mean score of 0.75, which is 80% higher than the overall average score of all industries. While the average efficiency of manufacturing of chemical raw materials and chemical products was the lowest with a mean score of 0.39. Results of sensitivity analysis show that profit has a great impact on the efficiency score of special equipment manufacturing firms, but a relatively weak impact on the firms which manufacture computers, communications, and other electronic equipment. The effect of export delivery value on efficiency score is not obvious.

An in-depth study of the operational efficiency of Internet banks is essential to enhance banks’ competitiveness and sustainable development. We use the two-stage network data envelopment analysis (DEA) model to divide the operational process of Internet banks into the value operation stage (stage 1) and the value creation stage (stage 2). This paper adopts the R&D investment that reflects the characteristics of Internet banks as the input of the value operation stage in the two-stage DEA model. It examines the operating efficiency of China’s Internet banks from 2018 to 2019, including stage efficiency and comprehensive efficiency. The empirical analysis results indicate that the contribution of stage 2 to the comprehensive efficiency is higher than that of stage 1. Similarly, it can also be shown that the average values of the stage efficiency and comprehensive efficiency of Internet banking in 2019 are higher than those in 2018. In addition, the Kruskal–Wallis test shows no significant difference in the average comprehensive efficiency of Internet banks in the three major economic belts. These results have significant strategic implications for managers, regulators, and policymakers who share a common interest in boosting financial sustainability and performance.

How to control industrial air pollution is an urgent policy problem to be solved. Through efficiency evaluation, we can test the implementation effect of the policy, to provide decision support for industrial development. China's provincial industrial system consists of production stage and emission reduction stage. Taking the number of industrial waste gas treatment facilities as a carry-over variable (i.e., connecting multiple periods), this study establishes a dynamic two-stage, slacks-based measure data envelopment analysis (SBM-DEA) model considering shared inputs to evaluate the operating efficiency of China's provincial industrial systems from 2011 to 2019. The results show that (1) The industrial waste gas treatment facilities (carry-over variables) have a significant impact on the overall efficiency and stage efficiency of the industrial system; (2) The average overall efficiency and average stage efficiency of the provincial industrial system have improved during 2011-2019, but they are still inefficient, and the inefficiency has certain geographical characteristics; (3) During the 12th Five Year Plan period (2011-2015), the average efficiency of the emission reduction stage was higher than that of the production stage, but the situation reversed during the 13th Five Year Plan period (2016-2019). This study is of great significance to policy makers of industrial air pollution control.

In China, industrial pollution has become an urgent problem for policy makers and enterprise managers. To better support industrial development, we need to determine the effectiveness of policies through efficiency evaluation. China’s provincial industrial system consists of two stages: production and emission reduction. The emission reduction stage is composed of three parallel sub stages: solid waste treatment, waste gas treatment and wastewater treatment. In this process, the treatment capacity of industrial wastewater treatment facilities can be used as carry forward variable, which is not only the desirable output of the previous emission reduction stage, but also the input of the current emission reduction stage. Therefore, this paper proposes a dynamic hybrid two-stage data envelopment analysis (DEA) model for eco-efficiency evaluation of industrial systems, and applies it to a case study of Chinese regional industry. Applying the data collected from 2011 to 2015 to the model, the following conclusions can be drawn: (1) During the whole survey period, the average eco-efficiency was 0.9027. The overall eco-inefficiency of China’s provincial industrial system during the study period is mainly due to low efficiency of solid waste treatment and waste gas treatment. (2) The average eco-efficiency of provincial industrial system increased steadily from 2011 (0.6448) to 2014 (0.6777), but decreased slightly in 2015 (0.5908). (3) The carry forward treatment capacity of industrial wastewater treatment facilities has a remarkable impact on provincial industrial system efficiency scores, especially at the wastewater treatment stage (0.6002 vs 0.3691). (4) Provincial industrial system exists distinct geographical characteristics of low efficiency. This study has important guiding significance for policy makers and enterprise managers who are concerned about industrial pollution control.

The operation process of the Chinese provincial industrial system consists of four stages, namely the production (P) stage, wastewater treatment (WWT) stage, solid waste treatment (SWT) stage, and sulfur dioxide treatment (SDT) stage. Based on this structure, a four-stage data envelopment analysis (DEA) model is developed to evaluate the eco-efficiency, production efficiency, wastewater treatment efficiency, solid waste treatment efficiency, and sulfur dioxide treatment efficiency of provincial industrial systems in China, considering the undesirable output and variable returns to scale (VRS). Based on the objective data from 2011 to 2015, the following conclusions are drawn: Firstly, the eco-efficiency of the Chinese provincial industrial system has not been significantly improved during the study period, and the average eco-efficiency score is low, only 0.3805. Secondly, the reasons for the low eco-efficiency of the industrial system are different in the Eastern, Central, Western, and Northeastern regions. Thirdly, compared with the P stage, industrial WWT stage, and SWT stage, the efficiency of SDT stage is still relatively weak.

In the 12th Five-Year Plan period, Sichuan province has selected strategic emerging industries such as new-generation information technology, new energy, high-end equipment manufacturing, new materials, biology, energy conservation and environmental protection. However, its strategic emerging industries are still in the stage of cultivation and development, and there are some problems such as low industrial agglomeration and obvious industrial restructuring. In essence, this is because the focus of the development of strategic emerging industries is not clear enough, and the location advantages are not fully utilized to select the most suitable strategic emerging industries for the development of the region. This paper constructs a regional strategic emerging industries selection decision model, applies ARCGIS to study the spatial distribution of strategic emerging industries in Sichuan province, and uses fuzzy comprehensive evaluation method (FCEM) and analytic hierarchy process (AHP) to solve the priority of developing strategic emerging industries. Conclusions: Firstly, Sichuan province should give priority to the development of new generation information technology industry and new energy vehicle industry, then high-end equipment manufacturing industry, energy-saving and environmental protection industry and new energy industry, and finally biological industry and new material industry. The larger the coefficient of influence is, the higher the comprehensive score is, while the larger the coefficient of sensitivity is, the lower the comprehensive score is. Secondly, the number of enterprises in the new-generation information technology industry and the new energy vehicle industry is still not dominant in Sichuan province. Finally, the study found that the current development of strategic emerging industries in Sichuan province is extremely unbalanced in various regions, and the phenomenon of competition and reconstruction is obvious.