Mankind is constantly pursuing economic growth and social development, and while these bring convenience to people, they involve large amounts of greenhouse gas emissions under the heavy use of energy, which produces quite serious air pollution, which not only affects people's health but also leads to ecological environmental damage. Nowadays, the impact of the environment and the negative impacts of the world's economies while promoting economic growth are the most important issues for economies to achieve balanced development. This study uses the dynamic slack‐based data envelopment analysis (DEA) model to assess the environmental energy efficiency of high‐income economies (including China) and explore the negative impacts on the environment, to obtain a basis for energy‐saving emission reduction methods or configurations by using 48 high‐income economies (including China) from 2010 to 2014. The interperiod (carryover) variable is gross domestic product (GDP). The empirical results show that economies with high energy efficiency have a large consumption of energy and are unable to reduce carbon dioxide emissions. We also find that there exists a difference in the results depending on whether GDP is considered as the carryover variable. European economies are more efficient in energy consumption than others, and Asian economies are the most inefficient. In order to pursue GDP growth, economies need to consider reducing their energy consumption and CO₂ emissions and improve their energy usage efficiency, further implementing sustainability.

This study aims to evaluate the performance of the machine tool industry in Taiwan between 2010 and 2014 by using a dynamic network data envelopment analysis (DN-DEA) model. Taiwanese machine tool industry had an improvement in productivity from 2010 to 2014. Under the two-stage DEA model, we find that productivity in the production and market stage represent a rapid growth, and the productivity growth of the market stage is greater than that of the production stage. The overall productivity growth is mainly attributable to the market stage productivity growth and partially to the production stage productivity growth.

The green total factor productivity (GTFP) has become an important index of balancing the energy consumptions, economic development, and environmental protections. This study extends the slacks‐based directional distance function (SDDF) of Fare and Grosskopf into the framework of the Malmquist–Luenberger productivity index to estimate the slack‐based GTFP. This SDDF can allow decision‐making units to explore the optimal direction and measure a more precise productivity index. For the empirical study, we employ the new model to measure the slack‐based GTFP for Chinese 30 provinces over the period of 2005–2017. Furthermore, we investigate the effect of the regional differences on the GTFP and the convergence of the Chinese GTFP with β‐converge and σ‐convergence. Our results show that there is a rapid improvement in the GTFP in China, which is attributed to the technical change. However, there exists a significant difference in GTFP for three regions of China. The eastern region performs significantly better than the others. Finally, we find there is a strong convergence in China, and the eastern region has a faster convergence than the other regions.

The importance of afforested area in environmental protection was emphasized in the 12th Five Year Period. Therefore, we first synthetically assess the afforested efficiency by non-oriented Network SBM model. The overall process is divided into two stages, and PM2.5, NO_x, and SO₂ are viewed as the linkage between two stages. Moreover, the afforested area is used as the final output in the second stage. According to the empirical results, the energy efficiency is higher than the afforested efficiency, and the low overall efficiency mainly due to the bad performance of afforested efficiency. The average afforested efficiency score was less than 0.42 in these five years, indicating that although the average afforestation area increased gradually, the afforestation efficiency did not improve significantly. Most provinces should take effective measures to control the emissions of PM2.5, NO_x, and SO₂ and gradually improve the afforested area.