Research on a Coordination Evaluation and Prediction Model of Water Use and Industrial Ecosystem Development

Int J Environ Res Public Health. 2023 Jan 29;20(3):2381. doi: 10.3390/ijerph20032381.

Abstract

Coordinating the relationship between water use and industrial ecosystem development is the key to ensuring high-quality and sustainable development of the industrial economy. In this paper, a model was proposed for evaluating and predicting the coordination between water use and industrial ecosystem development. First, aiming at the coordination of water use and industrial ecosystem development, this paper determined 15 indicators from the aspects of water demand and supply, water conservation and environmental protection, industrial sustainable development, input and output, and industrial development status. The combination weighting method based on game theory was used to determine the weight of the evaluation index. Then, the coordination evaluation model called the back propagation neural network (BP)-coupling coordination degree model (CCDM) and the coordination prediction model called gray models (GM)-BP-CCDM were established. Finally, the model was applied to the coordination evaluation and prediction of water use and industrial ecosystem development in the Hebei Province, China. The results show that the coordination degree of cities in the Hebei Province is moderate. Therefore, based on the research results, some scientific and reasonable suggestions for water resources utilization and industrial ecosystem development were put forward.

Keywords: BP–CCDM model; GM–BP–CCDM model; combined empowerment method; industrial ecosystem development; water resources utilization.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • China
  • Cities
  • Conservation of Natural Resources
  • Ecosystem*
  • Industrial Development*
  • Water

Substances

  • Water

Grants and funding

This research was funded by the Soft Science Research Special Project of Hebei Science and Technology Innovation Capacity Improvement Program from Hebei Provincial Department of Science and Technology (Grant No. 22557634D). This research was funded by the Humanities and Social Science Research Major Project of Hebei Education Department (Grant No. ZD202114). This research was funded by the research project of Hebei Province’s social development from Hebei Federation of Social Science Associations (Grant No. 20220202459).