Comprehensive Evaluation of Water Resource Carrying Capacity in Hebei Province Based on a Combined Weighting–TOPSIS Model

Nianning Wang, Qichao Zhao (), Lihua Yuan (), Yaosen Chen, Ying Hong and Sijie Chen
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Nianning Wang: School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
Qichao Zhao: School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
Lihua Yuan: School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
Yaosen Chen: Application Research Center of Spatial Information Technology, Geological Geomatics Institute of Hebei, Langfang 065099, China
Ying Hong: School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China
Sijie Chen: School of Remote Sensing and Information Engineering, North China Institute of Aerospace Engineering, Langfang 065000, China

Data, 2025, vol. 10, issue 9, 1-25

Abstract: Water scarcity severely restricts the sustainable development of water-stressed regions like Hebei Province. A scientific assessment of water resource carrying capacity (WRCC) is essential. However, single-weighting methods often lead to biased results. To address this limitation, we propose a combined weighting model that integrates the Entropy Weight Method (EWM), Projection Pursuit (PP), and CRITIC. To support this model, we developed a multi-dimensional, long-term WRCC evaluation dataset covering 11 prefecture-level cities in Hebei Province over 24 years (2000–2023). This approach simultaneously considers data dispersion, inter-indicator conflict, and structural features. It ensures that a more balanced weighting scheme is obtained. The traditional TOPSIS model was further improved through Grey Relational Analysis (GRA), which enhanced the discriminatory power and stability of WRCC assessment. The findings were as follows: (1) From 2000 to 2023, the WRCC in Hebei Province showed a fluctuating upward trend and a “high-north, low-south” spatial gradient. (2) Obstacle analysis revealed a vicious cycle of “resource scarcity–structural conflict–ecological deficit”. This cycle is caused by excessive exploitation of groundwater and low efficiency of industrial water use. The combined weighting–GRA–TOPSIS model offers a reliable WRCC diagnostic tool. The results indicate the core barriers to water use in Hebei and provide targeted policy ideas for sustainable development.

Keywords: WRCC; combined weighting; improved TOPSIS model; obstacle degree model; Hebei Province (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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