农田生物质能集约利用空间优化决策

发展农田生物质能源集约利用，是解决中国能源问题和环境问题的重要途径。由于农田生物质能空间分布的分散性、不连续性，所以对生物能源数量、空间分布特征以及其集约利用空间优化问题进行研究是生物质能源从研发走向实际应用的关键。该文以广东省为例，利用NPP（净初级生产力）模型获得可用农田生物质能生物量和空间分布状况，在此基础上，采用不同尺度的泰森多边形作为生物能源的初级筹集范围，应用遗传算法对生物质能集约利用的空间优化配置问题进行快速求解。结果表明，遗传算法和GIS模型结合对解决面域供应和点域需求问题具有明显的优越性，可为生物质能集约利用提供有效的空间优化方法；尺度效应对模型模拟结果有很大影响，当以10 km为邻近阈值建立泰森多边形作为初级筹集区域时，适宜度达到最大。该研究可为生物质能集约利用空间优化决策提供依据。

Optimal spatial decision of cropland bio-energy intensive application

Cropland bio-energy intensive application is an important way of solving energy and environmental problems in China. Since crop residues are not distributed centrally and continuously, the intensive application of cropland bio-energy is different from that of the traditional energy, i.e. coal, oil, natural gas, etc. Therefore, the studies of bio-energy quantity, distribution characteristics and the optimization of bio-energy intensive application are very important to help the intensive application of cropland bio-energy and the selection of optimal locations of power plants. A case study in Guangdong province, China, this paper provided a framework to quickly estimate the quantity of available cropland biomass energy and analyze its distribution pattern based on NPP model, and divide the primary collection regions by Thiessen polygon in several scales, and use genetic algorithms to optimize selecting the locations of cropland bio-energy intensive application. The results show that genetic algorithms and GIS model can solve the question of searching spatial demand point from polygon support area, and the MAUP can affect the results of GA model. When Thiessen polygon was built as primary collection regions by proximal-tolerance of 10 km, the GA model could get the best fitness values. The model can provide the effective spatial optimal method for cropland bio-energy intensive application.