老库区生态型反贫困决策分析—以四川省大洪河水库邻水县库区为例

我国老库区面临着严重的贫困和生态环境双重问题,而其贫困为典型的环境制约型贫困,贫困与生态环境问题交织在一起,互为因果.对于典型环境制约型贫困的老库区,只有打破贫困与生态环境之间的恶性循环,建立社会、经济和生态环境之间的良性循环才是老库区反贫困的根本途径.本研究以四川省大洪河水库邻水县库区为例,在对老库区反贫困与生态环境可持续性进行分析的基础上,采用不确定多目标规划方法,构建了四川省大洪河水库邻水县库区的生态型反贫困规划模型.通过系统分析和建立规划模型,既定性又定量地进行了四川省大洪河水库邻水县库区反贫困和生态环境可持续性研究,就整个邻水县库区而言,需进行移民8711人,新修梯田3188.24hm2,扩建经济林果园地1413.09hm2,新建人工草地85.47hm2,才有可能达到反贫困和提高生态环境可持续性的目标.

Strategies for Ecotypic Anti-poverty in Old Reservoir Regions:A Case Study of Linshui Reservoir in Sichuan Province

Reservoirs built and financed by the Government in 1950s~1970s contributed enormously to the national macroeconomic development and local people′s livelihood during that period. However, the construction of those reservoirs has deteriorated the natural environment and resource supply, leading to massive poverty in associated regions that occurs today. Furthermore, the insufficient migrant aftercare and governmental subsidies have made it difficult for locals to rebuild their lives. Therefore, to reduce the poverty in those regions we need to develop integrated strategies to transform the current social, economical and ecological structures to a manageable and sustainable establishment. As a case study, we analyzed the problems related to the poverty and eco-environmental sustainability in the Linshui Reservoir Region of Sichuan Province and established a sustainable development model using the uncertainty multi-objective planning framework. The results showed that to achieve the objective for the poverty reduction and eco-environmental improvement it was necessary to emigrate 8711 people, and establish 3188.24hm2 terrace, 85.47hm2 artificial grassland and 1413.07hm2 economic forests and orchards. The prediction provides an optimal land use structure at the regional level that could not only improve the eco-environment, but also allow locals to have sustainable economical gain. We believe that these strategies can be the key measures for poverty reduction and environmental improvement in all′old reservoir′ regions.