选择中国各省农村污水处理投资重点村

IF 4.3 Q1 ENVIRONMENTAL SCIENCES ACS ES&T water Pub Date : 2025-02-24 DOI:10.1021/acsestwater.4c00910
Wenjing Meng, Jiping Jiang*, Xiao Hu, Sijie Tang, Xunfeng Xia, Qiuhua Jian, Wenzhao Wang, Fengge Liu, Ruiyi Yang and Yi Zheng, 
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引用次数: 0

摘要

农村污水处理(RST)已成为中国环境恢复中的一个关键问题。与城市地区不同,农村地区RST的规划和管理要复杂得多,而且往往受到有限预算的限制。对省、市政府来说,确定乡村科技投资的优先级具有重要而紧迫的意义。本研究提出了一个乡村科技投资优先排序的决策模型,以平衡经济和生态效益与生命周期成本。该模型应用于中国广东、河南、湖南、山西、青海和辽宁等省的104246个村庄,并成功生成了每个省的优先级地图。结果表明,广东省前5%村的投资回报率(ROI)达到1.75,比随机投资高出20.3%。此外,相应的平均收入比随机选择的村庄高6倍。该模型在其他5个省份也表现出色。利用随机森林回归模型和SHAP方法进行的因素重要性分析表明,人口规模是影响优先级的最重要因素。该决策模型为优化RST投资,最大化边际效益,提高生态保护资金使用效率提供了科学、高效的解决方案。
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Select Priority Villages for Investments on Rural Sewage Treatment in Chinese Provinces

Rural sewage treatment (RST) has emerged as a critical issue in the restoration of China’s environment. Unlike in urban areas, the planning and management of RST in rural settings are significantly more complex and often constrained by limited budgets. It is significant and urgent for provincial or municipal governments to identify the villages with the highest priority for RST investment. This study proposes a decision-making model to prioritize villages for RST investment, balancing economic and ecological benefits with life-cycle costs. The model was applied across 104,246 villages in Guangdong, Henan, Hunan, Shanxi, Qinghai, and Liaoning Provinces in China, and it successfully generated priority maps for each province. Results indicate that the return on investment (ROI) for the top 5% of villages in Guangdong Province reached 1.75, which is 20.3% higher than that of random investments. Furthermore, the corresponding average revenue was 6 times greater than those of randomly selected villages. The model also demonstrated strong performance in the other 5 provinces. Factor importance analysis, utilizing random forest regression models alongside SHAP methodology, reveals that population size is the most influential factor in prioritization. The decision-making model offers scientific and efficient solutions for optimizing RST investments while maximizing marginal benefits and enhancing the efficiency of ecological protection funds.

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