China's rapid development over the past few decades has resulted in significant environmental pollution from CO2, SO2 and NO2 emissions amongst others, all of which can adversely affect human health. However, past efficiency analyses have tended to discuss energy efficiency and environmental efficiency separately, or have discussed ways in which governments can reduce pollution at the source. In other words, previous analyses of China's energy efficiency have only been focused on one-stage, and very few studies have integrated the environment, government pollution reductions, and economic development in the analysis. Therefore, to fill this research gap, this study examined the first and second-stage energy and air pollution reduction efficiencies in thirty Chinese provinces and municipalities from 2013 to 2016 using a dynamic, network slack-based measure (DNSBM) model, in which labor, fixed assets and energy consumption were the input variables in the first production stage, GDP was the output variable, and CO2, SO2, and NO2 emissions were the link variables, and in the second treatment stage, pollution treatment investment was the input variable, the CO2, SO2, and NO2 emissions reductions were the output variables, and energy consumption/GDP was the carryover variable. It was found that; (1) Beijing, Shanghai, Guizhou, Hainan and Qinghai had the best performances over the 4 years, and Shaanxi, Shandong, Fujian, Jilin, Liaoning and Xinjiang had the worst performances and therefore had the greatest need for improvements; (2) the overall provincial efficiencies were generally relatively low and significant improvements were needed however, there were large differences between the provinces/municipalities; (3) the overall air pollution reduction efficiency governance in the second stage was low, indicating that more effective measures were needed to improve environmental efficiency and that the dynamic developments in government regulations and governance had resulted in a policy implementation lag; and (4) in 21 provinces, the first-stage productivity was significantly higher than the second-stage treatment productivity, indicating that the treatment stage needed to be strengthened.
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Environmental Science(all)
- Strategy and Management
- Industrial and Manufacturing Engineering