基于分布式水文模型和福利成本法的生态补偿空间选择研究

空间选择是生态补偿研究的核心问题之一,选择合理的区域进行生态补偿有利于提高生态补偿项目的效率.以黑河流域上游肃南县为研究区域,运用分布式水文模型Soil and Water Assessment Tool(SWAT)对研究区实施生态补偿后的水源涵养增加量进行模拟,同时考虑土地利用类型转化成本以及生态系统服务丧失风险,采用福利成本法对研究区生态补偿空间选择进行研究.结果表明:黑河上游肃南段内不同子流域的生态补偿效率系数最高值为0.0394,最低值0.0131,相差明显；根据效率系数,采用聚类分析方法,可将各子流域划分为优先补偿区、次级优先补偿区和潜在补偿区,分批进行补偿；采用空间选择后生态补偿效率较不采用时可提高54.5％.

Spatial targeting of payments for ecosystem services Based on SWAT Model and cost-benefit analysis

Payment for ecosystem services(PES), an essential market based instrument for translating non-market environmental services into financial incentives, is being rapidly applied to promote ecosystem restoration and preservation among landowners or land-users, in an effort to retain ecosystem services or regain those lost through by human activities. In this context, spatial targeting is one of the most important issues in the study of payment for ecosystem services. A failure to establish spatial priorities in the targeted area when establishing a scheme for payment will reduce the efficiency of the payment system. So, specifying appropriate work areas is a key method for implementing programs of payment for ecosystem services, and will significantly increase the program's efficiency. In this article, an applied spatial targeting tool was constructed. This tool considers three spatial variables, including 1) restored hydrological services estimated by the Soil and Water Assessment Tool (SWAT), 2) risks of losing ecosystem services, and 3) costs of land-use transformation. To demonstrate functions of the targeting tool, a case study identifying spatial priorities in several sub-watersheds was implemented in the upper reaches of Heihe River, Sunan County, Gansu Province, China. In this study, spatial and attribute data of the study area, collected from the Environmental and Ecological Science Data for western China, were used to drive the SWAT model. The average monthly runoff data measured at the Yingluoxia hydrological station were used to calibrate of the model's parameters and to improve simulation accuracy. Then, each sub-watershed' hydrological services were measured for estimating the ecosystem services after PES program were implemented. The risk of losing ecosystem service in each sub-watershed was calculated based on data related to grassland degradation provided by the Pasturage Bureau of Sunan County. The third variable, the cost of land-use transformation, consists of three parts: opportunity cost, protection cost, and transaction cost. The opportunity cost was estimated by focusing on areas of pasture which are the most likely alternative to natural grassland. Researchers assumed natural grassland itself has no commercial value, as land-users are prohibited from pasturing on grassland designated for use in the payment program for ecosystem services. The opportunity cost is calculated as the actual landowners or land-user's economic loss, reflecting their willingness to accept the program's restrictions. The protection costs were represented by government's payment for protection to landowners such as the costs of grass seeds, railings, etc. while the transaction costs were the government's payment for grassland restoration. All of the abovementioned indices were integrated to calculate the efficiency of payment for ecosystem service as measured for each individual watershed. Last, all watersheds were ranked based on the efficiency index to prioritize areas for which offering payment for ecosystem service is used as part of this program. Results of the case study show the efficiency indices of different sub-watersheds range from 0.0131 to 0.0394. Based on the calculated values of the efficiency index, all sub-watersheds were classified into three hierarchical levels: priority payment zones, sub-priority payment zones and low priority payment zones. The program efficiency of payment for ecosystem services in Sunan County increased by 54.5% when using the spatial targeting tool. We also concluded financial efficiency of the PES program could potentially be increased using the targeting tool. Nevertheless, the study suggests the potential ability to increase the efficiency of each spatial attribute depends largely on its variation in space. In summary, when implementing programs of payment for ecosystem services, it is essential to identify main environmental problems which need to be resolved and the ecosystem services which need to be restored, and to apply targeting tools to establish priorities for the areas to be protected across the entire program region.