Hang Lin’s research while affiliated with Fujian Normal University and other places

The rapid development of China’s economy has largely relied on energy consumption, which has caused serious air pollution and affected public health, and economic development, energy consumption, air pollution, and public health have nowadays become the focus of academic attention. However, the previous literature failed to consider undesirable output when constructing the Dynamic Network DEA model to study the efficiencies of energy consumption, air pollution, and public health. As a result, past studies did not employ those three issues in a structure to effectively reflect and solve the problems. Therefore, this paper constructs the Two-Stage Dynamic Undesirable DEA model and puts energy consumption, air pollution, and public health into the same framework in order to fill the gap in the literature. Findings show that the production consumption efficiency stage is better than the health protection stage, and that the efficiency values of variables vary significantly in different regions. The efficiency of tumor and tuberculosis is the lowest, with oil consumption and birthrate efficiencies are the best, followed by coal, nitrogen oxide (NOx), and dust efficiencies. Coal efficiency exhibits a fluctuating downward trend, whereas the efficiencies of electricity, air pollutants, tuberculosis, and tumor tend to fluctuate upwards during the research period. In consideration of the varying performances of different regions in the two stages, we put forward suggestions based on these findings to improve the efficiencies of energy, environment, and public health in China.

Innovation ability has become one of the core elements in the pursuit of China’s green growth, and high-tech industries are playing a leading role in technological innovation in China. With the rapid development of China’s high-tech industries, their innovation efficiency has attracted widespread attention. This article aims to illustrate a shared inputs two-stage network Data Envelopment Analysis (DEA), to measure the innovation efficiency of high-tech industries in China’s 29 provinces from 1999 to 2018. The results indicate that there are obvious differences in the innovation efficiency of the provinces. The technology development efficiency, the technical transformation efficiency, and the overall innovation efficiency of the developed east coast provinces are generally higher than those of the backward central and western provinces. This article further applies the spatial econometrics model to analyze the factors influencing the innovation efficiency of high-tech industries. We have found that government support, R&D input intensity, industries aggregation, economic extroversion, and the level of development of the modern service industries cause varying degrees of impact on innovation efficiency.