通过对流域生态补偿现状进行分析研究，从生态效果、经济水平、社会公平及政策投入4个方面构建流域生态补偿效果评估指标体系；在此基础上，针对评估指标较多、专家评判时容易出现一致性差等问题，提出一种抖动机制改进的SobolSSA-ANP (sobol sparrow search algorithm-analytic network process)方法，并基于该方法对太湖流域的生态补偿效果进行评估。评估结果表明：太湖流域生态补偿实施效果介于一般和较好，且倾向于较好；从2010年到2020年，通过实施生态补偿政策，太湖流域生态补偿效果呈逐渐上升趋势；为进一步提升生态补偿效果，太湖流域需加大资金投入和提升政策满意度。为此，流域可推动多主体参与的横向生态补偿政策以拓宽资金渠道，实现生态资源到生态资本的转化。研究结果可以为流域生态补偿效果的评估和生态补偿政策的制定提供借鉴和参考，对提升流域治理能力现代化水平具有重要的实际意义。

To improve the watershed ecological environment, and to realize the sustainable development of water resources, government and scholars have taken a series of explorations. Combined with the flexibility of Payment for Watershed Ecosystem Services and the efficient management of government, China proposed watershed Ecological Compensation and has piloted numerous Ecological Compensation programs in Xin'an Jiang, Taihu, and other watersheds. The implementation of Ecological Compensation has played an important role in the improvement of watershed ecology, and it has also impacted the economy, society, and other aspects of watersheds. Therefore, it’s crucial to assess the impacts of Ecological Compensation on the ecology, economy, and society to further promote the implementation and improvement of Ecological Compensation policy. Taken Taihu Basin as a case study area. Firstly, the evaluation index system, which includes the ecology, economy, social equity, and government investment is established. The ecology subsystem includes the per capita water resource, eutrophication index, and cyanobacteria density. The economy subsystem includes the total water supply, per capita disposable income, pollutant emission per unit Gross Domestic Product, and the proportion of tertiary industry. The social equity subsystem includes disposable income ratio between urban and rural, health security number of beds per 10 thousand people. The government investment includes the investment in ecological protection. Then, based on the Dithering Sobol Sequence improved Sparrow Search Algorithm and Analytic Network Process, the evaluation model SobolSSA-ANP is constructed. In the proposed model, the Analytic Network Process is adopted to analyze the influence relationship among indexes, and to determine the evaluation matrix; then the Sobol Sequence improved Sparrow Search Algorithm model is adopted to optimize the coefficients in the matrix dynamically. Subsequently, the optimized coefficients are input into the Analytic Network Process model to reconstruct the evaluation matrix and to assess the Eco-Compensation effect. The results show that: (1) The SobolSSA-ANP improves the consistency and stability of the Analytic Network Process model, and the evaluation results are more accurate. The CR values decrease by 1~10 orders of magnitude in the multiple ranges given by the experts, and the convergence results are more stable than the Analytic Network Process model. (2) The implementation of Eco-compensation in the Taihu Basin was effective, and has improved the ecological environment. Especially, the effect from 2017 is significantly better than that before 2012. (3) The Eco-compensation fund is the most important factor that influences the Eco-compensation effect. The weight of the Eco-compensation fund is 0.191421, which is higher than the other indexes. Thus, it’s necessary to explore the market-based and multi-agent Eco-compensation mechanisms to broaden the source of Eco-compensation funds. (4) The eutrophication index and cyanobacteria density are the second and thirdly important factors, respectively. That is consistent with the actual problems in the Taihu Basin. The improvement of the above two factors will improve the ecological environment in the Taihu Basin greatly. The implementation of Eco-compensation in the Taihu Basin has promoted the improvement of ecology, economy, and social equity. However, the Eco-compensation funds, the design and implementation of Eco-compensation mechanism required to be enhanced. It is suggested to promote innovation of Eco-compensation mechanisms, to explore the multi-agent and market-based Eco-compensation, and to establish a dynamic evaluation mechanism for watershed Eco-compensation. Meanwhile, establishing the ecological information sharing mechanism, and transferring the ecological resource to ecological capital, promoting the sustainable development of watershed. The proposed model and results can provide reference for the evaluation of watershed EC effect, and are effective in improving the modernization of the governance capacity. In the future research, the blockchain and marker-based EC mechanism can be coupled together to improve the trading mechanism of ecosystem services. Meanwhile, compare the effect of different EC mechanisms to exploring the optimal EC mechanism.

国家自然科学基金(42001250); 中央高校基本科研业务费(B200201032) ; 常州市领军型创新人才引进培育项目(CQ20210095)