Evaluation of the Utilization Efficiency of Water Resources in China Based on ZSG-DEA: A Perspective of Water–Energy–Food Nexus

Abstract

The main resources needed for human survival are water, energy and food, and the research on the relationship among the three resources has become the research topic. Due to the shortage of natural resources, research on the efficiency of water–energy–food (WEF) nexus has become a hot issue among scholars and governments all over the world. The simple models cannot study the correlation among them; the methods of WEF nexus gradually shift from single to integrated model. The data envelopment analysis (DEA) model is the most simple and effective model, which can measure the efficiency value of each decision-making unit (DMU) and overcomes the shortcoming that the evaluation index system needs to give the weight. The DEA can calculate the efficiency value, but it cannot improve the efficiency value of DMU by changing the input. In this paper, the zero-sum gain (ZSG) DEA model is constructed based on zero-sum game theory. The initial efficiency values of 30 provinces in China are calculated, and each DMU is proportionally allocated according to the proportional subtractive method. After several iterations, the efficiency values of all DMUs reach 1, indicating that global optimization has been achieved. Under the condition of effectiveness, the initial input water resources are reconfigured. Finally, some conclusions are made, and optimization strategies are proposed for 30 provinces in China with low water resource utilization efficiency.

A Review of Water-Energy-Food Nexus Development in a Just Energy Transition

“…The WEF nexus approach emphasizes the importance of considering water, energy, and food systems as interconnected components of a larger ecosystem [33]. Managing these resources in isolation can lead to unintended consequences and suboptimal outcomes.…”

“…Energy systems often rely on water resources for cooling and hydropower generation, while agriculture requires energy inputs for irrigation and processing [42]. By considering these interdependencies, policymakers can design energy transition pathways that safeguard water availability and support sustainable agricultural practices, ensuring long-term food security [30][31][32][33].…”

Spatial Association Network and Driving Factors of Agricultural Eco-Efficiency in the Hanjiang River Basin, China

“…Scholars have gradually improved the model based on research needs [28][29][30][31], such as research based on zero-sum game theory to construct the ZSG-DEA model, which overcomes the shortcomings of evaluating indicator systems that need to be weighted to address the issue of water-energy-food efficiency relationships. It is the most widely used research method because it is less restrictive in data handling [32].…”

“…The average efficiency of the agricultural eco-efficiency spatial network in the HRB from 2010 to 2020 was 0.89, indicating that individuals within the basin have high stability in connecting to the network structure. This means that the mobility of factors between the two cities is poor, making it difficult to promote factor flow through the agricultural eco-efficiency spatial correlation network and reducing the comparative advantage between cities [32]. This requires synergistic basin-wide policies.…”

A Comparative Study of the Driving Factors of Water Resources Use Efficiency in China’s Agricultural and Industrial Sectors

“…The super-efficiency DEA model [17], three-stage DEA model [18], slacks-based measure (SBM) model [19], undesirable output super efficiency slacks-based measure (SE-SBM) model [20], network DEA model [21], and other various DEA improvement methods have been developed. In the spatial dimension, WRUE has been measured at the urban [22], provincial [23,24], basin [19,25], and national [21,26] levels. Scholars have evaluated WRUE in different water use sectors of agriculture, industry, the domestic sphere, and ecology.…”