Analysis and prediction of variations in resource and environmental carrying capacity in Xuzhou utilizing panel PVAR and GWO-SVM models

Regional resource and environmental carrying capacity ( RECC ) is a crucial foundation for sustainable development. This paper analyzes the spatiotemporal evolution, geographical disparities, and contributing factors of Xuzhou’s RECC from 2011 to 2023, and projects its development trend by 2030. A comprehensive evaluation index system was developed, incorporating water resource development intensity and RECC, and using the entropy weight method, the Dagum Gini coefficient, Panel Vector Autoregression (PVAR), Monte Carlo simulation, and the Grey Wolf Optimizer-Support Vector Machine (GWO-SVM) for analysis. The findings indicate that Xuzhou's RECC had a variable upward trajectory with accelerated improvement post-2016; spatially, its capacity ranked as follows: Coordinated Development Zone > Core Urban Area > Revitalization Support Zone. Inter-regional disparities were the principal source of overall regional differences, accounting for 68.55% of the total regional disparities. The primary factors associated with were prioritized as the magnitude and intensity of water resource development, Ecological Buffer and Resilience Capacity, socio-economic pressure, water usage structure and efficiency, and resource base support capacity, all of which contribute comparably. The GWO-SVM model forecasts that by 2030, Pizhou and Xinyi county-level cities will reach the maximum level of RECC, while Feng County will remain at the lowest level. These findings establish a solid foundation for developing tailored methods to promote the coordinated and high-quality advancement of Xuzhou.

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Publication Details

Journal
Discover Sustainability
Published
2026-06-28
DOI
https://doi.org/10.1007/s43621-026-03737-1
Primary Topic
Water Resources and Sustainability
Type
article
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article

Analysis and prediction of variations in resource and environmental carrying capacity in Xuzhou utilizing panel PVAR and GWO-SVM models

仲崇翔, Ying Yang, Lei Zhou, Zhipeng Zhou et al.
Discover Sustainability
Water Resources and Sustainability
article

Analysis and prediction of variations in resource and environmental carrying capacity in Xuzhou utilizing panel PVAR and GWO-SVM models

仲崇翔, Ying Yang, Lei Zhou, Zhipeng Zhou, Min Tan, Yuting Dai, Nan Jiang, Wei Jing, Junfeng Qu, Yanzhi Wang
article en

Abstract

Regional resource and environmental carrying capacity ( RECC ) is a crucial foundation for sustainable development. This paper analyzes the spatiotemporal evolution, geographical disparities, and contributing factors of Xuzhou’s RECC from 2011 to 2023, and projects its development trend by 2030. A comprehensive evaluation index system was developed, incorporating water resource development intensity and RECC, and using the entropy weight method, the Dagum Gini coefficient, Panel Vector Autoregression (PVAR), Monte Carlo simulation, and the Grey Wolf Optimizer-Support Vector Machine (GWO-SVM) for analysis. The findings indicate that Xuzhou's RECC had a variable upward trajectory with accelerated improvement post-2016; spatially, its capacity ranked as follows: Coordinated Development Zone > Core Urban Area > Revitalization Support Zone. Inter-regional disparities were the principal source of overall regional differences, accounting for 68.55% of the total regional disparities. The primary factors associated with were prioritized as the magnitude and intensity of water resource development, Ecological Buffer and Resilience Capacity, socio-economic pressure, water usage structure and efficiency, and resource base support capacity, all of which contribute comparably. The GWO-SVM model forecasts that by 2030, Pizhou and Xinyi county-level cities will reach the maximum level of RECC, while Feng County will remain at the lowest level. These findings establish a solid foundation for developing tailored methods to promote the coordinated and high-quality advancement of Xuzhou.

Discover Sustainability
China University of Mining and Technology (CN)
Openalex Percentile: Top 14%
Water Resources and Sustainability
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