In the face of severe water pollution, all provinces and cities in China have actively invested in water environment management funds driven by the goals of national energy conservation and emissions reduction. However, due to differences in natural environment, economic and technological levels, industrial structure, and other aspects in provinces and cities, their water environment management effects are also different across time and space. Under economic development and environmental regulation policies, it can be seen that the change in industrial GDP is not completely consistent with that of industrial wastewater discharge. How to improve desirable outputs and reduce undesirable outputs under the limited investment in water pollution control are key issues when investigating the efficiency of industrial water pollution control. This study uses the Dynamic SBM (Slacks-Based Measure) model to assess wastewater resources for research samples covering the 30 regions of China. There are two output variables, two input variables, and one carry-over variable. The output variables are industrial wastewater treatment and industrial output, the two input variables are industrial water consumption and facility operation cost, and the carry-over variable is industrial waste. This study concludes with implications for theory research, as these variables may lead to a better understanding and merging with the input variables, output variables, and carry-over variable of recent studies. The empirical results show that from the efficiency rank changes of the 30 regions for 2011–2015, regions with higher industrial output do not appear to have improved versus other regions, such as for Shandong, Guangdong, Jiangsu, Qinghai, and Zhejiang. The 30 regions’ efficiency scores show some volatility, with 13 regions’ efficiency score volatility clustering close to 0, like Beijing, Chongqing, Shandong, Guangdong, and Sichuan. In contrast, for Anhui, Inner Mongolia, Zhejiang, and Xinjiang, their efficiency scores fell more than other regions in this period and thus should adjust their input/output variables to increase their efficiency scores. This study further presents that many lower-/middle-/high-industrial output regions do not achieve a balance between industrial output and industrial wastewater treatment. How to find a balance between these two factors for any region is a vitally important issue for industrial wastewater treatment policy makers. Under such a circumstance, an industrial output region may not actually be highly efficient at doing this.

When a developing country is undergoing a rapid growth period, agricultural wastewater, domestic wastewater, industrial wastewater, and organic matter content in chemical oxygen demand (COD) usually increase in great amounts, causing environmental pollution. Thus, this paper proposes a summary of factors to assess the performance of wastewater discharge costs. Total fixed assets, population growth, and wastewater treatment expenses in various regions of China were used as input factors, while gross regional product, discharged wastewater, and discharged COD were used as output factors. We employed the directional distance function (DDF) method to compare 31 regions of China between 2011 and 2015. The results showed that areas with leading economic development and areas with a small population and vast natural land have good wastewater treatment efficiency. In the past five years, economic development and wastewater treatment expense efficiency in Chongqing have been improving, such that by the end of 2015, this region efficiency was approaching frontier efficiency. We also found that the efficiency of wastewater treatment expense in many areas often falls below 0.6, which is still very low. There is, thus, a large gap between the regions and the leading frontier regions, meaning that the efficiency of wastewater treatment expense needs to be improved.

This research adopts the meta Dynamic Directional Distance Functions (DDF) model in order to calculate the environmental efficiency and environmental governance efficiency of China’s industrial sector from 2010 to 2017 from the overall, sub-regional, and sub-provincial perspectives and discusses the technical gaps in regional environmental pollution control and the reasons for ineffective environmental governance. The research results show that the overall level of environmental governance efficiency in China’s industrial sector is relatively high over this time period, and the group frontier calculation results have improved compared to the meta frontier. The actual technical level of the high-income group is closest to the potential technical level, and the upper-middle income group is still far from the potential technical level. The main reason for the ineffective environmental governance of the provinces in the high-income group is ineffective management, while the main reason for ineffective environmental governance of the provinces in the upper-middle-income groups is technical inefficiency. Regardless of high-income groups or upper-middle-income groups, each province’s inefficiency of environmental governance is caused by inefficiency of the input factors.

Coal-based thermal power generation has long been the main source of power generation in the mainland of China. The efficiency of power generation is an important factor that determines the energy conservation and emission reduction as well as the sustainable development of the power industry in China. By comparing the regional differences of 30 provinces in the mainland from 2013 to 2017, this study uses the Super-DDF model and the TFEE to comprehensively evaluate the energy efficiency of thermal power generation. Empirical results: Overall efficiency: eastern efficiency (1.181) is the highest, followed by western (0.956), central (0.951) and northeastern (0.926). Total factor energy efficiency: eastern efficiency (0.923) is the highest, followed by western (0.754), central (0.742) and northeastern (0.710). The government and power industry managers should fully consider the regional differences in the field of thermal power generation when formulating policies so as to improve the power efficiency and promote the green development of power industry in China. Based on the analysis results, although the coal-fired power industry is more mature than other alternative energy industries, the expansion of thermal power generation cannot be considered if CO₂ emissions are to be reduced. Additionally, the market share and competitiveness of the local power industry can be increased based on the different conditions of the resource endowments of each region.

The purpose of this study is to explore the impact of pollution control on industrial production efficiency in 31 provinces and cities in the Yellow River and Non-Yellow River basins in China from 2013 to 2017, using the methods of the directional distance function (hereinafter referred to as DDF) and the technology gap ratio (hereinafter referred to as TGR) in parallel, while taking the industrial production sector (labor force, total capital formation, energy consumption and industrial water consumption) and the pollution control sector (wastewater treatment funds and waste gas treatment funds) as input variables. Undesirable outputs (total wastewater discharge, lead, SO₂ and smoke and dust in wastewater) and an ideal output variable (industrial output value) are taken as output variables. It is found that the total efficiency of DDF in the Non-Yellow River Basin is 0.9793, which is slightly better than 0.9688 in the Yellow River Basin. Among the 17 provinces and cities with a total efficiency of 1, only Shandong and Sichuan are located in the Yellow River Basin. The TGR values of 31 provinces, cities and administrative regions are less than 1, and the average TGR value of the Yellow River Basin is 0.3825, which is lower than the average TGR value of the Non-Yellow River Basin of 0.5234. We can start by improving the allocation of manpower and capital, implementing the use of pollution prevention and control funds, improving the technical level of industrial production, improving pollutant emission, and increasing output value to improve overall efficiency performance. This study uses the parallel method, taking the industrial production department and the pollution control department as inputs, to objectively evaluate the changes in industrial production efficiency and technology gap in the Yellow River and Non-Yellow River basins, which is conducive to mastering the situation of pollution control and industrial production efficiency, and provides the reference for SDG-6- and SDG-9-related policy making.

This study evaluates the impact of industrial energy efficiency on agricultural development in the 31 member countries of the Organization for Economic Cooperation and Development (OECD) from 2015 to 2019. Using dynamic network slack-based measures (DN-SBM) and dynamic network total factor productivity (DN-TFP) indicators, dynamic cross-period information is used to assess the changes in efficiency and productivity of the industrial and agricultural sectors. The empirical results show that the industrial sector of the OECD is more efficient than the agricultural sector, and while some countries have low efficiency, productivity tends to improve. The study has three contributions: 1. Using the concept of the water–energy–food (WEF) nexus as a framework and combining its elements with variables to evaluate the efficiency performance of OECD countries; 2. using a dynamic two-stage DN-SBM model to objectively assess the overall efficiency value and provide improvement suggestions for different stages; 3. a comprehensive analysis of efficiency and productivity; the results can serve as a reference for OECD countries when formulating policies