Table 1 Main research directions of this topic.
Aspect | Key indicators | Literature | Research methods | Main findings |
|---|---|---|---|---|
Industrial water use/wastewater treatment efficiency | COD, NH4–N, water consumption | Wang et al.10 | SBM-DEA; Shadow Price | There are still great potentials to reduce water consumption and pollutants’ discharge and great geographic disparities in different areas |
COD, ammonia nitrogen | Fujii and Managi11 | WRDDM | The results indicate that wastewater management efficiency improved in the eastern and central regions. However, there is a significant efficiency gap between provinces in the western region | |
Total energy consumption; industrial value added; industrial wastewater emissions | Yang and Li12 | SBM-DEA and MATLAB programming | TFE of wastewater control in the industrial sectors is still far from optimal, and low wastewater control has become one of the obstacles to its sustainable development | |
Industrial solid waste; assets | Ren et al.13 | Dynamic modified SBM-DEA | In recent years the average efficiency of NYREB in many provinces shows a declining trend, and the average efficiency of solid waste treatment in provinces of YREB is mostly concentrated at a high level The total efficiency scores under the influence of urbanization are generally higher than that without the influence of urbanization level. Urbanization level has a significantly positive impact on wastewater output efficiency in each region Water pollution disease efficiency and the total efficiency of the eastern, western, and central regions all show a decreasing trend | |
COD; urbanization rate water diseases; wastewater treatment capacity; COD | Sun et al.14 Sun et al.15 | Dynamic exogenous variable SBM-DEA Dynamic network SBM-DEA | ||
Wastewater treatment plants | GHG; COD; climate type eco-efficiency; carbon footprint; CO2; techno-economic efficiency; technological gap ratios; concentration of pollutants | WRDDM.; Combining DEA with uncertainty assessment | The operational costs and greenhouse gas emissions are the main drivers reducing eco-productivity WWTPs in eastern and western China significantly outperform those in the central region in terms of mean efficiency and performance stability | |
An et al.26 | ESDA model; super-efficiency DEA; Malmquist index | From 2011 to 2015, urban wastewater discharge showed a spatial agglomeration trend Both technological upgrade and scale-up efficiency are negative, leading to low overall efficiency | ||
Zhang et al.27 | Statistical data analysis | Unbalanced population distribution and economic development led to differences in the efficiency of wastewater treatment plants between regions | ||
Non-radial DEA | The efficiency levels for the studied sample of WWTPs are low Plant size, quantity of eliminated organic matter, and bioreactor aeration type are significant variables affecting the energy efficiency of WWTPs | |||
Huang et al.30 | Applied energy | The study evaluated the energy efficiency of wastewater treatment plants in the Yangtze River Delta and gave perspectives on regional discrepancies | ||
Jiang et al.31 | SBM-DEA | Large WWTPs operate more efficiently than small ones. Of these, 170 wastewater treatment plants are relatively efficient, with a score of 1; 691 low-efficiency samples have different degrees of excess input or insufficient output | ||
WU-WT system (water use and water treatment) | Water use; capital invested; wastewater treatment: wastewater discharge cost capital | Mixed network two-stage SBM-DEA model; | In the past ten years, the WU efficiencies are often higher than the WT efficiencies The WT efficiencies are often lower than the WU efficiencies during 2006–2015 | |
Bi-level programming (BLP) and DEA | It is found that water systems can be cost-effective only when both water use and wastewater treatment subsystems are cost-effective | |||
Urban wastewater treatment efficiency (UWWTE) | Length of sewage pipeline; daily treatment capacity; total amount of wastewater treated; dry sludge | Bian et al.34 | Dynamic DEA | In China the main reason for the low efficiency of regional urban sewage purification systems is the poor sewage purification effect The results show that the overall UWTE is at a low level, as evidenced by the fact the average efficiency score is 0.51 during 2008–2017, and no cities have an efficiency score equal to 1 in the Yangtze River Economic Belt |
Pan et al.35 | Bootstrap-DEA model and Malmquist index | |||
Agricultural water use efficiency | Number of agricultural workers; agricultural water consumption; agricultural fixed assets | Wang et al.25 | SFA and spatial econometrics | AWUE of all provinces showed an upward trend during the observation period with obvious spatial correlation and unbalanced development of provinces |
Labor; capital water resources; agricultural production; greywater | Modified gravity model; SBM-DEA; social network analysis method; QAP | The overall trend of AWUE in China has been fluctuating and declining, and the structure of AWUE spatial network in China is complex and relatively stable with close inter-provincial connection and obvious spatial spillover effect. Geographical proximity, technological development level, farmers’ income, and natural resource endowment have a significant impact on the development of AWUE network |
