# Dynamic Evaluation and Internal Driving Factors of Water Resources Green Efficiency in China
## 2 Literature Review  
The study of water resource utilization efficiency mainly includes evaluation methods, evaluation index systems and influencing factors. The evaluation methods of water resource utilization efficiency mainly include single index evaluation and total factor evaluation. Single index evaluation is the ratio of water resources input to a certain indicator to reflect water resources utilization efficiency in a certain field [6; 7; 8], but this method has limitations, such as one-sided result and strong subjectivity. The total factor evaluation method is based on the input-output ratio of the overall factors in the production process, which can evaluate the efficiency objectively and truly. Total factor water resource utilization efficiency evaluation methods include Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA). [PERSON] [9] used stochastic frontier analysis to measure agricultural irrigation water utilization efficiency, and was then widely used to evaluate water use efficiency [10; 11; 12]. However, the SFA model cannot consider multiple outputs, and the DEA model can make up for the shortcomings of the SFA model. [PERSON] et al. [13], [PERSON] and [PERSON] [14] measure the economic efficiency of water resources based on traditional radial DEA models, and some scholars use non-radial and non-angular SBM model [15], super-SBM model [16], and [PERSON] and [PERSON] [17] constructed a Malmquist productivity index that dynamically evaluates efficiency. With the deterioration of the water environment, scholars have gradually paid attention to the environmental efficiency of water resources. The environmental efficiency of water resources is generally based on the economic efficiency of water resources with the addition of unexpected outputs [18; 19]. Scholars use non-radial and non-angular SBM models that consider undesired outputs to measure water resource environmental efficiency [20; 21]. In addition, Malmquist-Luenberger productivity index model can also evaluate water resource environmental efficiency [22; 23]. Regarding the formulation of the index system, without considering water environmental pollution factors, scholars mostly use water consumption, labor force, and capital investment as input factors, and the output factor is economic development. When considering water environmental pollution, some scholars use water pollution as an input indicator [24; 25], but most scholars use water pollution as an output indicator, such as [PERSON] et al. [26]. [PERSON] et al. [27] used sewage as the single undesired output indicator. [PERSON] and [PERSON] [28], and [PERSON] et al. [29] used multiple undesired output indicators such as chemical oxygen demand and ammonia nitrogen emissions. At the same time, it is necessary to point out that the existing literature also has water resources green efficiency studies, such as regarding China's green water efficiency [30], and industrial green efficiency [31; 32], but these articles are still based on environmental water efficiency studies and fail to include social development, ecological factors, and the essential differences of water resources green efficiency included in this study. As for the driving factors of water resources utilization efficiency, scholars mostly study the external influencing factors of water resources utilization efficiency by modeling. [PERSON] et al. [33] used the Log Mean Divisia Index (LMDI) model to find that economic development, increased industrial proportion, and population influx can drive the increase in industrial wastewater discharge in various provinces, and technical progress can reduce wastewater discharge. [PERSON] et al. [34] used the simplified Laspeyres model to study the driving factors of industrial water use in Tianjin and found that technical progress has driven down industrial water consumption, while industrial scale has increased water consumption. [PERSON] et al. [35], [PERSON] et al. [27], and [PERSON] et al.