Ecological indicator system construction for water resources and pollution governance in Guangxi (2004-2022) and coupling-coordination assessment

Abstract

Guangxi’s rapid urbanization and rising environmental pressures have made balanced water-ecology governance a core regional policy concern. Using annual data for prefecture-level cities in Guangxi, China, from 2004 to 2022, this study constructs an ecological indicator system encompassing “water resources-pollutants-clean governance” and conducts a comprehensive evaluation with a coupling-coordination diagnosis. Data sources included the Guangxi Statistical Yearbook, the China Environmental Statistical Yearbook, and relevant editions of the Guangxi Statistical Bulletin of National Economic and Social Development. Methodologically, indicators were first standardized via minimum-maximum scaling. Using the driver-pressure-state-impact-response framework, indicators were selected. Objective weights were determined through the criteria importance through the intercriteria correlation method. The technique for order preference by similarity to the ideal solution method was used to compute composite scores for the three subsystems. We then established models for the coupling degree (C) and coupling-coordination degree (D). Across 2004-2022, the D ranged from 0.458 to 0.798, indicating predominantly low-to-moderate coordination with discernible phase fluctuations rather than extreme imbalance. In 2022, the headline metrics were measured at 0.958 for C and 0.798 for D, representing the upper end of the observed coordination level over the sample period. Overall, urban ecological governance in Guangxi remained only partially coordinated during 2004-2022, although with episodic improvements. Policy pathways should prioritize high-weight, high-dispersion dimensions to enhance subsystem matching and raise coordination.

Keywords

Water resources management / Pollution control / Ecological governance / Urban sustainability / Guangxi / Indicator system / Wastewater treatment

Funding

This project was funded by the Humanities and Social Sciences Research Project of the Ministry of Education of China: Research on Cultural Genetics and Contemporary Remodeling of Landscape Formation of Han and Tang Villages (23XJC760003), the Shaanxi Provincial Social Science Foundation Project: Research on Survey Data Mining and Resource Value of Revolutionary Cultural Relics in Shaanxi (2023GM03), and the Shaanxi Provincial Social Science Planning Project: Research on Evaluation Indicator System of Outstanding Popularization Achievements in Social Sciences (2023ZD1825).

Conflict of interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

References

[1] Zhao, YW. Research on the evaluation method of water pollution prevention effect of urban landscape ecological river. Environ Sci Manage. 2023; 48(2):184-189. doi: 10.3969/j.issn.1673-1212.2023.02.036

[2] Minying, W, Qing, G, Bohu, L, Maoyun, Q, Chenye, W. Hot, spots, evolutionary trends and future outlook of China’s sustainable development research in the past three decades: An analysis based on citespace knowledge mapping. China Popul Resour Environ. 2023; 33:148-159. doi: 10.12062/cpre20221202

[3] Yang, Y, Cao, F. Spatial and temporal differentiation pattern of urban water resource utilization efficiency in the central plains urban agglomeration. Henan Sci. 2024; 42(10):1497-1504. doi: 10.3969/j.issn.10043918.2024.10.011

[4] Minying, W, Qing, G, Bohu, L, Maoyun, Q, Chenye, W. Improvement of watershed ecological compensation in china from the perspective of policy effectiveness. World For Res. 2023; 36(1):6. doi: 10.13348/j.cnki.sjlyyj.2022.0104.y

[5] Wang, W. Corporate environmental responsibility and government subsidies - an analysis based on rent seeking perspective. Res Financ Issues. 2020; 11:9. doi: 10.19654/j.cnki.cjwtyj.2020.11.011

[6] Pires, A, Morato, J, Peixoto, H, Botero, V, Zuluaga, L, Figueroa, A. Sustainability assessment of indicators for integrated water resources management. Sci Total Environ. 2017; 578:139-147. doi: 10.1016/j.scitotenv.2016.10.217

[7] Khan, N, Tabasi, ZA, Liu, J, Zhang, BH, Zhao, Y. Recent advances in functional materials for wastewater treatment: From materials to technological innovations. J Mar Sci Eng. 2022; 10(4):534. doi: 10.3390/jmse10040534

[8] Amann, M, Kiesewetter, G, Schöpp, W, et al. Reducing global air pollution: The scope for further policy interventions. Philos Trans R Soc A. 2020; 378( 2183):20190331. doi: 10.1098/rsta.2019.0331

[9] Mathey, J, Hennersdorf, J, Lehmann, I, Wende, W. Qualifying the urban structure type approach for urban green space analysis-a case study of dresden, Germany. Ecol Indic. 2021; 125:107519. doi: 10.1016/j.ecolind.2021.107519

[10] Makumbura, RK, Mampitiya, L, Rathnayake, N, et al. Advancing water quality assessment and prediction using machine learning models, coupled with explainable artificial intelligence (XAI) techniques like shapley additive explanations (SHAP) for interpreting the black-box nature. Results Eng. 2024; 23:102831. doi: 10.1016/j.rineng.2024.102831

[11] Ma, T, Sun, S, Fu, G, et al. Pollution exacerbates China’s water scarcity and its regional inequality. Nat Commun. 2020; 11(1):650. doi: 10.1038/s41467-020-14532-5

[12] Cheng, X, Fang, L, Mu, L, Li, J, Wang, H. Watershed eco-compensation mechanism in China: Policies, practices and recommendations. Water. 2022; 14(5):777. doi: 10.3390/w14050777

[13] Khan, SAR, Ponce, P, Yu, Z, Golpîra, H, Mathew, M. Environmental technology and wastewater treatment: Strategies to achieve environmental sustainability. Chemosphere. 2022; 286:131532. doi: 10.1016/j.chemosphere.2021.131532

[14] Krellenberg, K, Artmann, M, Stanley, C, Hech, R. What to do in, and what to expect from, urban green spaces- Indicator-based approach to assess cultural ecosystem services. Urban For Urban Green. 2021; 59:126986. doi: 10.1016/j.ufug.2021.126986

[15] Jacobson, MZ, von Krauland, AK, Coughlin, SJ, et al. Low-cost solutions to global warming, air pollution, and energy insecurity for 145 countries. Energy Environ Sci. 2022; 15(8):3343-3359. doi: 10.1039/D2EE00722C

[16] Ahmed, AA, Sayed, S, Abdoulhalik, A, Moutari, S, Oyedele, L. Applications of machine learning to water resources management: A review of present status and future opportunities. J Clean Prod. 2024; 441:140715. doi: 10.1016/j.jclepro.2024.140715

[17] Islam, N, Rogers, ES, Schoenthaler, ED, Thorpe, LE, Shelley, D. A cross-cutting workforce solution for implementing community-clinical linkage models. Am J Public Health. 2020; 110(S2):S191-S193. doi: 10.2105/AJPH.2020.305692

[18] Wu, L, Ma, T, Bian, Y, Li, S, Yi, Z. Improvement of regional environmental quality: Government environmental governance and public participation. Sci Total Environ. 2020; 717:137265. doi: 10.1016/j.scitotenv.2020.137265

[19] Guangxi Zhuang Autonomous Region Bureau of Statistics. Guangxi Statistical Yearbook 2024. China:China Statistics Press; 2024. Available from: https://tjj.gxzf.gov.cn/tjsj/tjnj/?utm_source [Last accessed on 2025n Nov 16].

[20] National Bureau of Statistics, and Ministry of Ecology and Environment. China Environment Statistical Yearbook 2024. China: China Statistics Press; 2025. Available from: https://www.shujuku.org/china-environment-statistical-yearbook-2024.html [Last accessed on 2025n Nov 16].

[21] Guangxi Zhuang Autonomous Region Bureau of Statistics. Statistical Communiqué of Guangxi Zhuang Autonomous Region on the 2024 National Economic and Social Development. Guangxi Zhuang Autonomous Region Bureau of Statistics; 2025. Available from: https://tjj.gxzf.gov.cn/tjsj/tjgb/qqgb/t19769919.shtml?utm_source [Last accessed on 2025n Nov 16].

[22] Diakoulaki, D, Mavrotas, G, Papayannakis, L. Determining objective weights in multiple criteria problems: The critic method. Comput Oper Res. 1995; 22(7):763-770. doi: 10.1016/0305-0548(94)00059-h

[23] Chengjun, W, Sqing, Z, Tao, F, Chuanjia, DU, Binglin, Z. Evaluation of human-water harmony based on coupled coordination model. Peoples Yellow River. 2024; 8: 87-93. doi: 10.3969/j.issn.1000-1379.2024.08.016